Yves Gallet

Maxime Le Goff

Agnès Genevey

Triaxe archeointensity analysis

Keywords: Archeomagnetic intensity data, experimental protocol, Triaxe magnetometer, cooling rate effect

Since 2004, numerous archeomagnetic intensity data have been obtained using the vibrating sample magnetometer Triaxe, which measures full-vector magnetization directly at high temperatures, in either an applied or zero field. Satisfactory comparisons have been made between Triaxe intensity data and results derived from more conventional Thellier-Thellier type techniques, indicating the reliability of Triaxe data. For each specimen analyzed, a Triaxe archeointensity value is obtained from the average of R'(Ti) data. The R'(Ti) parameter is determined every 5°C and corresponds to the ratio, multiplied by the laboratory field intensity, between the natural remanent magnetization (NRM) and laboratorythermoremanent magnetization (TRM lab ) fractions that are lost between reference temperature T1 and a given temperature Ti between T1 and reference temperature T2. Here, we introduce an additional parameter, based on so-called AutoR'(Ti) data, to facilitate and improve the interpretation of Triaxe measurements. Each individual AutoR'(Ti) datum corresponds to an averaged R'(Ti) value; the AutoR'(Ti) dataset is then obtained by gradually decreasing the temperature range from T1 to T2 to a minimum temperature interval near T2. Several examples of Triaxe measurements show the value of using AutoR'(Ti) data to isolate the most appropriate temperature range for an intensity determination, as well as to characterize the cooling rate effect on TRM acquisition. In particular, these experiments confirm that the Triaxe procedure minimizes this effect because, when it is present, it appears to be largely due to magnetic grains with high unblocking temperatures (>~350°C). Moreover, the AutoR'(Ti) dataset provides alternatives for estimating mean archeointensity values at both the fragment and fragment-group levels. We show that the simple approach used so far, based on the average of the R'(Ti) data determined over a single temperature interval, provides results as reliable as those derived from other options.

Introduction

Archeomagnetism, based on the analysis of the magnetic properties of baked-clay archeological artifacts, is a unique tool for tracing the evolution of the Earth's magnetic field over the past ten millennia. In general, the strength of this discipline lies in the precision of the dating of the material studied and that of the experimental determinations. However, the reliability and accuracy of archeomagnetic intensity data have been subject to numerous evaluations based on selection criteria that vary from one study to another [START_REF] Genevey | ArcheoInt: An upgraded compilation of geomagnetic field intensity data for the past ten millennia and its application to the recovery of the past dipole moment[END_REF][START_REF] Paterson | On improving the selection of Thellier-type paleointensity data[END_REF]Hervé et al., 2019a;[START_REF] Brown | Global archaeomagnetic data: the stateof-the-art and future challenges[END_REF]. It appears that archeointensity data are often of uneven quality; biased (e.g., due to no or insufficient correction for the cooling rate effect on thermoremanent magnetization acquisition) or erroneous (e.g., due to undetected heating-induced magnetominerological alteration) values are likely present in the data compilations (e.g. [START_REF] Licht | Ensembles of low degree archeomagnetic field models for the past three millennia[END_REF]. Although a set of selection criteria is clearly useful, it remains very difficult to identify all inaccurate values, with the intent to eliminate them or at least reduce their detrimental effect on the development of archeo-geomagnetic field models [START_REF] Constable | Persistent high paleosecular variation activity in southern hemisphere for at least 10000 years[END_REF][START_REF] Hellio | Time-correlation based regression of the geomagnetic field from archeological and sediment records[END_REF][START_REF] Campuzano | Emergence and evolution of the South Atlantic Anomaly revealed by the new paleomagnetic reconstruction SHAWQ2k[END_REF][START_REF] Pavón-Carrasco | SCHA.DIF.4k: 4,000 years of paleomagnetic reconstruction for Europe and its application for dating[END_REF].

For nearly 20 years, our research group has been conducting archeointensity studies in different regions of the world, with a focus on Western Europe and the Near East [START_REF] Genevey | Geomagnetic field intensity variations in Western Europe over the past 1100 years[END_REF]2016;2021;[START_REF] Gallet | Archaeomagnetism at Ebla (Tell Mardikh, Syria). New data on geomagnetic field intensity variations in the Near East during the Bronze Age[END_REF][START_REF] Gallet | New Late Neolithic (c. 7000-5000 BC) archeointensity data from Syria. Reconstructing 9000 years of archeomagnetic field intensity variations in the Middle East[END_REF]2020). In recent years, these studies have utilized the Triaxe magnetometer and an original experimental protocol adapted to this instrument that were developed in our laboratory (Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. Comparative studies featuring other paleo-archeointensity protocols [START_REF] Thellier | Sur l'intensité du champ magnétique terrestre dans le passé historique et géologique[END_REF][START_REF] Coe | Paleo-intensities of the Earth's magnetic field determined from Tertiary and Quaternary rocks[END_REF][START_REF] Aitken | Determination of the intensity of the Earth's magnetic field during archaeological times: Reliability of the Thellier technique[END_REF][START_REF] Yu | Toward an optimal geomagnetic field intensity determination technique[END_REF] have shown the reliability of the intensity measurements obtained using the Triaxe protocol [START_REF] Genevey | Evidence for rapid geomagnetic field intensity variations in Western Europe over the past 800 years from new archeointensity French data[END_REF][START_REF] Hartmann | Archeointensity in Northeast Brazil over the past five centuries[END_REF][START_REF] Hartmann | New historical archeointensity data from Brazil : Evidence for a large regional non-dipole field contribution over the past few centuries[END_REF][START_REF] Hervé | Fast geomagnetic field intensity variations between 1400 and 400 BCE: new archaeointensity data from Germany[END_REF][START_REF] Shaar | Synchronizing geomagnetic field intensity records in the Levant between the 23 rd and 15 th centuries BCE: chronological and methodological implications[END_REF]. Nevertheless, we have remained concerned about the reliability of the Triaxe intensity data and are always striving to strengthen it.

The Triaxe is a vibrating sample magnetometer with a sensitivity of ~10 -8 Am 2 , allowing to quasi-continuously measure the full vector of the remanent magnetization of a small cylindrical specimen (<1 cm 3 ) directly at high temperatures of up to 650°C, in either a zero field or in a field in any direction, and at a fixed intensity up to 200 µT (Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]Fig. 1). While taking measurements, it demagnetizes the natural remanent magnetization (NRM) of the specimen, which, in the case of ceramic samples, consists of thermoremanent magnetization, as well as acquires (and subsequently demagnetizes) a laboratory thermoremanent magnetization (TRM lab ) of the specimen with a direction that is automatically adjusted according to that of the characteristic magnetization carried by that specimen. Further details on the principles of measurements with a vibrating sample magnetometer can be found in [START_REF] Poidras | Controlled atmosphere vibrating thermomagnetometer (C at VSM): a new device to optimize the absolute paleointensity determinations[END_REF].

The Triaxe archeointensity protocol developed by Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF] has been applied to thousands of archeological baked-clay specimens of various ages and origins (e.g., pottery, pavement, architectural brick or kiln) from different geographical regions (see above references). Since the original publication, we have developed further our procedure for analyzing Triaxe measurements. Although it has been used routinely for several years, it has not been presented until now. Therefore, the objective of this study is to describe in more detail the additional analyses performed to support and consolidate the interpretation of Triaxe measurements, and revisiting the question of the cooling rate effect on TRM acquisition. For archeointensity data, this effect is arguably difficult to estimate quantitatively (e.g. [START_REF] Fox | Cooling-rate dependence of thermoremanent magnetization[END_REF][START_REF] Genevey | Intensity of the geomagnetic field in western Europe over the past 2000 years: new data from ancient French pottery[END_REF][START_REF] Genevey | Eight thousand years of geomagnetic field intensity variations in the eastern Mediterranean[END_REF]Hervé et al., 2019b;[START_REF] Kostadinova-Avramova | Study of cooling rate effect on baked clay materials and its importance for archaeointensity determinations[END_REF]; however, the Triaxe method largely allows it to be taken into account experimentally via a simple and direct approach.

Description of the Triaxe archeointensity protocol

The Triaxe protocol is derived, albeit with major differences, from a method initially proposed by [START_REF] Boyd | A new method for measuring paleomagnetic intensities[END_REF] and then apparently never exploited beyond the original publication. It also shares common features with the method developed by [START_REF] Wilson | Palaeomagnetism in Northern Ireland. Part I. The thermal demagnetization of natural magnetic moments in rocks[END_REF] in that the magnetization measurements are continuously carried out at high temperatures and the NRM is replaced by a laboratory TRM in a single heating step. The constancy of the magnetization ratio as a function of temperature is then used to determine an intensity value (e.g. [START_REF] Lhuillier | Palaeointensities of Oligocene and Miocene volcanic sections from Ethiopia: field behaviour during the Cainozoic[END_REF]. The basic philosophy of the Triaxe method is to replace the NRM with the laboratory-TRM (TRM lab ) under similar field conditions (direction and intensity).

The procedure consists of five series of automatic measurements taken over a period typically lasting just over two hours (~2h-15min). Before starting the measurements, three parameters are set. The first two parameters are the temperatures referred to as T1 and T2. T1 is set low, generally at 150°C, i.e., at a temperature generally sufficient to eliminate most of the possible viscous remanent magnetization component. T2 is usually set at around 500°C, a temperature at which most of the NRM is isolated, but it can be set higher or lower depending on the thermal demagnetization of the specimen. T1 and T2 determine the range of temperatures over which the intensity analyses will be carried out. The third parameter is the field intensity (H lab ) for TRM lab acquisition. This acquisition is routinely performed with a cooling rate of 25°C per minute, but this rate, which is also considered a parameter, can be adjusted (see below). Note that the laboratory field intensity must be set as close as possible to the expected intensity (see discussion in Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF].

The five series of measurements, referred to as M1 to M5, are as follows (Fig. 2): M1: In a zero field, after preliminary heating from room temperature to T1, the magnetization of the sample is measured continuously up to T2. This procedure leads to the demagnetization of the NRM up to T2. M2, M3: Still in a zero field, the sample is cooled back to T1 (M2) and then reheated to T2 (M3). These two steps whose reversibility attests the thermal stability of the magnetization fraction remaining blocked at T2 allow the thermal variation in the spontaneous magnetization (Js) between T1 and T2 to be characterized (e.g. [START_REF] Dunlop | Rock magnetism, Fundamental and Frontiers[END_REF].

The effect of the magnetization fraction blocked at T2 on the magnetic fractions isolated between T1 and T2 is then taken into account by subtracting each M3 value measured at a given temperature from the M1 and M5 values (see below) measured at the same temperature, thus isolating only the fraction of magnetization involved in the archeointensity experiments. M4: After setting H lab , the magnetization is measured while the sample is cooled to T1. This step leads to the acquisition of TRM lab . The direction of H lab is automatically adjusted so that the TRM lab is closely parallel to the characteristic NRM. M5: In a zero field, the magnetization is measured while the sample is again heated to T2, demagnetizing the TRM lab .

The procedure ends with a rapid cooling of the sample to room temperature.

The derivation of an archeointensity value from the measurements above is explained in detail by Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. The method uses M1, M3 and M5, which are acquired with the temperature increasing at a rate of 30°C per minute. The archeointensity value is then estimated from the ratio between the NRM and TRM lab fractions lost between T1 and any temperature Ti between T1 and T2.

The intensity value R'(Ti) is obtained at Ti by the formula: R'(Ti)= H lab x Δ'1 (Ti) / Δ'5 (Ti) where

Δ'1(Ti) = (M1(T1) -M1(Ti)) -(M3(T1) -M3(Ti)) and Δ'5(Ti) = (M5(T1) -M5(Ti)) -(M3(T1) -M3(Ti)).
For each specimen, it is therefore possible to determine a mean archeointensity value averaging all R'(Ti) data points obtained between T1 and T2 (after interpolation every 5°C and exclusion of the four first data points, for which the NRM and TRM lab lost fractions are too small to provide a meaningful ratio, as often seen at the beginning of R'(Ti) curves). As a selection criterion, the R'(Ti) values should be fairly constant, although they often have a small slope (<10%; S parameter, Table 1) due to the cooling rate effect on TRM acquisition, which appears, based on our Triaxe experiments, to be weaker for magnetic grains with lower unblocking temperatures (Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]; see also [START_REF] Kostadinova-Avramova | Study of cooling rate effect on baked clay materials and its importance for archaeointensity determinations[END_REF][START_REF] Schnepp | New archeomagnetic secular variation data from central Europe, II: Intensities[END_REF]. Hence, Triaxe archeointensity data are not derived from the ratios between the NRM and TRM lab fractions remaining between Ti and T2, i.e., the R(Ti) dataset (Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. Doing so would enhance the influence of the magnetic grains with high unblocking temperatures, leading to an increase of R(Ti) values with temperature. Note, however, that for a relatively large number of fragments, the R(Ti) data show only a weak increasing trend with temperature, indicating that the cooling rate effect is often small or even negligible.

When a secondary magnetization component is detected from the thermal demagnetization above T1, for instance, a partial TRM due to firing in a violently destructive or, more simply, culinary, context, it is necessary to reduce the temperature range to one between temperature T1' (>T1) and T2 so that the intensity determination is carried out only on the primary magnetization acquired by an archeological artifact (Fig. 3, Table S1). An important advantage of the Triaxe protocol is that the intensity values do not need to be corrected for the anisotropy effect on TRM acquisition because the laboratory TRM is acquired in a direction as close as possible to that of the NRM (e.g. Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. Moreover, acquiring a near-complete TRM lab in a single heating step in place of the NRM reduces the effect associated with the presence of multi-domain grains.

The AutoR'(Ti) dataset, an additional parameter for Triaxe analysis

Examination of the thermal demagnetization diagram obtained from Triaxe measurements (M1 series) allows the different magnetization components carried by a specimen to be distinguished, thus guiding the choice of the temperature interval from which the R'(Ti) data will be taken in such a manner that that only the constant-direction fraction of the NRM is involved in calculating a mean archeointensity value (Fig. 3). Interestingly, the large number of measurements performed during the thermal demagnetization of the NRM can reveal the cases in which the primary component is preserved only in the highest temperature range, while a secondary component is largely predominant in the total magnetization. This situation would probably be more difficult to capture with a stepwise demagnetization performed every 25°C or 50°C.

The presence of a secondary magnetization component can also be demonstrated using the set of mean R'(Ti) values obtained by gradually reducing the temperature range from temperatures between T1 and T2 to temperatures only between T2-δT and T2, with δΤ ~50°C (i.e., establishing a minimum of 10 data points with which to estimate the average R'(Ti) value). Hereafter, T2-δΤ is referred to as T2'. The curve so obtained is called the AutoR'(Ti) S1). The AutoR'(Ti) values increase from T1 (150°C) up to about 280°C-360°C, depending on the specimens, and the values then stabilize for higher temperatures. Each stabilized segment corresponds to the temperature range in which just the characteristic magnetization component is isolated (in rare cases, the AutoR'(Ti) data show apparent oscillations, but these have no particular significance). Between T1' and T2', the R'(Ti) and AutoR'(Ti) values are very close to each other. The R(Ti) and AutoR'(Ti) curves also have similarities but the R(Ti) curves are much less exploitable, in particular because of the cooling rate effect on TRM acquisition (see below). We further note that in each case, every AutoR'(Ti) value for the stabilized segment should ideally be associated with a weak slope (a maximum value of 10% for the S parameter is used as a selection criterion for the R'(Ti)-based results; Table 1).

Nevertheless, it appears that slopes can be >10% at certain temperatures but this situation has no significant impact on the overall evolution of the AutoR'(Ti) data. The most important feature to check here is the stabilization of the AutoR'(Ti) values between T1' and T2' or at least over a significant segment of this temperature interval (see below).

The results shown in Figures 4 andS1 indicate that there may be two possible ways to determine a Triaxe archeointensity value for a specimen: 1) use the mean R'(Ti) value between T1' and T2; and 2) average the AutoR'(Ti) values obtained between T1', or a temperature close to T1', and T2' when these values are nearly constant. Our numerous Triaxe measurements show that the results for (1) and ( 2) are fairly equivalent to within 1 or 2 µT, at most. In practice, however, while AutoR'(Ti) data are essential to determine the temperature range over which a mean intensity value can be derived from the R'(Ti) data, the use of AutoR'(Ti) data to calculate an archeointensity value may not prove to be so straightforward (see Section 4). So far, we have utilized the first approach only and verified that the temperature T1' derived from the thermal demagnetization diagram marks a stabilization of the AutoR'(Ti) values. It is important to specify that specimens for which the AutoR'(Ti) curve never stabilizes between T1' and T2' will be systematically rejected, even if the R'(Ti) curve is substantially constant over this temperature range, because the average of the R'(Ti) data then depends critically on the chosen temperature T1'. Non-stabilization may occur for a variety of reasons, such as a large overlap of the demagnetization spectra of two magnetization components (which should be detectable via thermal demagnetization analysis), inappropriate behavior of the magnetic mineralogy, or, as we will see in Section 4, the cooling rate effect on TRM acquisition. In the latter case, specific complementary analyses can help to solve the problem.

Cooling rate effect

The Triaxe archeointensity results are considered to take the cooling rate effect on TRM acquisition into account because the R'(Ti) parameter allows us to reduce the influence of the magnetic grains most affected by this effect (Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. This cooling rate effect can be further examined by comparing the intensity values obtained for different cooling rates in TRM lab acquisition. Such experiments, which can be performed on either true archeological NRM or pseudo-NRM (i.e., a TRM acquired in known field and thermal conditions), can thus be helpful in strengthening the R'(Ti)-derived intensity data routinely acquired using a cooling rate of 25°C/minute. For instance, [START_REF] Salnaia | New archeointensity data from Novgorod (North-western Russia) between c. 1100 and 1700 AD. Implication for the European intensity secular variation[END_REF] analyzed true NRM from brick fragments from Novgorod, Northwestern Russia using two cooling rates (25°C/minute and 2°C/minute). Below, we present other examples that make use of AutoR'(Ti) data.

A pseudo-NRM of six baked-clay artifacts of different geographic origins and ages is examined first (Fig. 5 andS2). The pseudo-NRM are acquired using a cooling rate of 2°C/minute at laboratory-field intensities varying between 35 μT and 75 μT. Analyses are then performed between 150°C and ~500°C using cooling rates of 25°C/minute, 10°C/minute, and 2°C/minute for TRM lab acquisition. This sequence requires that the Triaxe be used for four days for each specimen. Note that the constant laboratory cooling rate of 2°C/minute for pseudo-NRM acquisition was not chosen to reproduce the original cooling rate (undoubtedly not constant) that prevailed for true-NRM acquisition faithfully; rather, our experiments allow for a better characterization of the cooling rate effect when (pseudo-)NRM and TRM lab are acquired under similar thermal conditions, the only difference being the constant cooling rates applied. The R(Ti), R'(Ti), and AutoR'(Ti) datasets obtained for specimens from France, Angkor (Cambodia), and Axum (Ethiopia), for which magnetization is mainly carried by minerals from the magnetite family with various levels of impurities [START_REF] Genevey | Evidence for rapid geomagnetic field intensity variations in Western Europe over the past 800 years from new archeointensity French data[END_REF] 2021 and in preparation), are shown in Figures 5 andS2 (other series of measurements, not presented, give similar results). Two essential features of the Triaxe method are identified, namely, the increase in the R(Ti) values with temperature (blue symbols filled with white) while the R'(Ti) values (blue symbols) remain nearly constant over the entire temperature range used for the intensity calculation. The increase in R(Ti) values depends on the fragments, i.e. their variable cooling rate effects. As observed by Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF], the increase in R(Ti) values slows when the cooling rate for TRM lab acquisition approaches the rate used for the pseudo-NRM acquisition. At the same time, the R'(Ti) values always remain nearly constant, whatever the cooling rate, making impossible to detect a notable (>5%) or systematic bias as a function of the cooling rate used (Table S1; recall that the R'(Ti) values are averaged over the entire temperature range between T1 and T2, which reduces the influence of a small shift towards the highest temperatures).

Contrary to the cases presented in Figure 4, an increase in AutoR'(Ti) values (red symbols) with temperature is observed. In more detail, it can be seen that after a segment of rather constant values, close to the R'(Ti) values, extending up to 300°C, sometimes to 350°C, the AutoR'(Ti) values increase significantly. Again, this increase depends on the cooling rate for TRM lab acquisition. This evolution is not surprising due to the definition of the AutoR'(Ti) values, i.e., the more the temperature range used for their calculation shrinks to high temperatures, the more the magnetic fraction involved is the one most affected by the cooling rate effect. The AutoR'(Ti) data provides more information on the range of unblocking temperatures for which the magnetic grains are minimally affected by the cooling rate effect; in this regard, it seems that the limiting temperature is often close to 300°-350°C. Thus, at this stage, it appears that the constancy of the AutoR'(Ti) values over the whole temperature range used for the calculation of the R'(Ti) averages is not a determining element; it appears to be more important to look for a constant evolution of these values over a significant segment of temperatures above T1, i.e., over ~150-200°C (here, up to 300°C-350°C), before an eventual inflection point is reached, signaling an increase over the highest temperatures.

This point is further illustrated by the analysis of true archeological NRM. Figure 6 displays the data obtained for three pottery fragments sampled from the same archeological context in the Near East. A specimen thermal demagnetization diagram is shown for each fragment (left diagrams). For clarity, only the R'(Ti) (blue symbols; Table S1) and AutoR'(Ti) (red symbols) data obtained from four specimens from the same fragment, analyzed with either a cooling rate of 25°C/minute (symbols filled with white) or 2°C/minute for TRM lab acquisition, are reported on the right diagrams. In all but one case, the AutoR'(Ti) values show an inflection point around 300-350°C, before the beginning of an increasing trend. Before this inflection point, the R'(Ti) and AutoR'(Ti) data are close. Among the different AutoR'(Ti) curves, the one obtained for a specimen of fragment Lot31-01 shows neither stabilization nor inflection (red dots filled with white, Fig. 6b), making it impossible to determine a reliable archeointensity result from the R'(Ti) curve (the latter curve is thus missing from the corresponding figure, and the specimen must be rejected). As seen previously, using a cooling rate of 2°C/minute instead of 25°C/minute for the TRM lab acquisition significantly dampens the increase in AutoR'(Ti) values. However, increases are still present, and sometimes shifted towards higher temperatures, indicating that the archeological cooling rate for these fragments was likely slower than 2°C/minute. Although making the AutoR'(Ti) data more difficult to exploit for the determination of quantitative intensity, these results show the reliability of the R'(Ti) values. However, we certainly cannot exclude the possibility that some specimens or fragments analyzed according to the Triaxe protocol could occasionally slightly overestimate archeointensity values (>5%; recall the slope for the R'(Ti) data). This uncertainty highlights the importance of studying several specimens per fragment from a group of several fragments taken from different artifacts found in the same archeological ensemble (the latter "overall" mean intensity value then being used for geomagnetic or archeological inferences). Still, a cooling rate of 10°C/minute, instead of 25°C/minute, could be used for TRM lab acquisition. Even though any gain in reliability would be difficult to evaluate, this alternative does not pose a problem: the time needed to analyze a specimen would remain less than 3 hours and would not fundamentally change the "productivity" of a working day.

Discussion

The interpretation of the Triaxe measurements is based on several selection criteria (see Table 1). They are few in number because our approach remains relatively qualitative, relying on the results of numerous experiments conducted on various archeological artifacts.

Indeed, not all the elements mentioned above are systematically quantified. This concerns the use of the AutoR'(Ti) data in particular, even though it is possible to calculate an average for these data over their stable segment. It is quite simple to deal with a stabilized segment that persists up to the highest temperature. When this is not the case, the calculation of the average of the AutoR'(Ti) values requires a precise determination of the temperature range used. This quantification leads to additional selection criteria with arbitrarily chosen threshold values of questionable efficiency. In our approach, the AutoR'(Ti) data are above all crucial to constrain the temperature interval over which to average the R'(Ti) data (i.e. the choice of temperature T1') or possibly reject the data. Nevertheless, we recognize that when Triaxetype magnetometers become commercially available and the associated analysis method is commonly used by other research groups, these additional criteria will allow for better homogenization of Triaxe-derived archeointensity results. Currently, we use what appears to be the simplest method, one that takes into account the largest possible magnetization fractions and minimizes the cooling rate effect on TRM acquisition as much as possible. Note that the selection criteria used could lead to rejected fragments, for which a significant cooling rate effect would preferentially concern the magnetic grains with low or moderate unblocking temperatures. Another crucial condition, one that obviously impacts all intensity methods, is that any intensity determination for a specimen be performed on its primary (single-vector) magnetization. Finally, it is important to remember that the reliability of all our archeointensity results is constrained by consistency tests carried out both on each fragment (now from a minimum of two specimens, often three studied per fragment, with a 5% limit around the average values) and a group of fragments coming from the same archeological context (with a minimum of three fragments and holding the standard deviation around the mean to less than 5 µT and 10%).

There are several possible approaches to select and use Triaxe intensity measurements.

For the determination of an archeointensity value at the fragment level, it would, for instance, be possible to use an approach quite similar to that of [START_REF] Shaar | Large geomagnetic field anomalies revealed in Bronze to Iron Age archeomagnetic data from Tel Megiddo and Tel Hazor, Israel[END_REF], i.e., examine the averages of all combinations of the mean R'(Ti) data available for the different specimens (i.e., the AutoR'(Ti) data over their stabilized segment with, for each combination, a single mean R'(Ti) datum per specimen) with S and K parameters of less than 10% and greater than 50%, respectively. Here, we calculate the mean of all averages and the square root of the mean of all variances. This "global" approach relies on a large number of averages, generally more than several thousand, depending on the amount of AutoR'(Ti) data generated per specimen and the number of specimens per fragment, whereas the current method relies on a single average, i.e., a single AutoR'(Ti) datum at temperature T1', for each specimen. To illustrate the two options, a comparative test was performed for two groups of fragments from Tell Begum, Iraq (BG39-40; [START_REF] Gallet | Tracing the geomagnetic field intensity variations in Upper Mesopotamia during the Pottery Neolithic to improve ceramicbased chronologies[END_REF] and Qatna, Syria (SY03; [START_REF] Livermore | Archeomagnetic intensity variations during the era of geomagnetic spikes in the Levant[END_REF]. The results given in Table 2 (see the columns referred to as "Single average" and "All averages") show that the differences are within the error bars and can therefore be considered to be negligible.

Accordingly, using the two sets of means per fragment to derive a mean at the fragment group level yields statistically identical results (columns headed "Mean (1)" and "Mean (2)", Table 2). Furthermore, it is also possible to analyze the group means derived from all combinations of AutoR'(Ti) data from all specimens and fragments in the same group. These combinations are so numerous that it is necessary to randomly reduce their number. This reduction was performed by taking into account the respective number of combinations between the different fragments. Doing so introduces a de facto weighting of the data, on the one hand, according to the number of specimens studied per fragment and, on the other hand, according to the temperature range selected for each specimen (the wider the range, the greater the number of AutoR'(Ti) values). Such calculations are tedious, but, in the end, the results are statistically identical to those obtained previously (column Mean (3), Table 2). When applied to a fragment, the global approach does introduce no, or very little, weighting according to the specimens because the temperature ranges selected for the different specimens are often very similar. At this stage, it thus appears that the simple method we have used so far (single average per fragment, then a single average of the fragment intensity values), plus consistency tests per fragment and per group of fragments, allows most of the (small) differences induced by the different options to be eliminated, making these options statistically equivalent.

It should be stressed that larger differences might occur in the estimation of the uncertainties in the means per fragment group. In the present case, as in our previous publications, these uncertainties are measured via the variance of the means per fragment, whereas a more complete calculation might also take into account the mean of all the variances per fragment, with the resulting variance being the sum of the two. For the Tell Begum data (Mean (1) and Mean (2); Table 2), the standard deviations would change from 1.2 µT and 1.1 µT to 1.3 µT and 1.4 µT, respectively, while the standard deviations of the group means from the Qatna-SY03 data would increase from 3.2 µT and 3.1 µT to 3.6 µT and 3.5 µT, respectively. For the latter calculations, the uncertainties of the R'(Ti) averages obtained per specimen were not taken into account; had they been used, Mean (1) for Tell Begum and Qatna would have been 34.7 ± 1.7 µT and 78.6 ± 3.7 µT, respectively. The simplicity of these calculations should allow them to be generalized, at least taking into account the uncertainties in intensity values determined per fragment when estimating a mean value for a group of fragments.

Finally, the Triaxe magnetometer can be used for more than archeointensity determinations, as it can be used to test Thellier's laws (independence, reciprocity and additivity) for partial TRM or in an examination of the evolution of the magnetic viscosity as a function of temperature (see discussion in Le [START_REF] Goff | Potential of high-temperature magnetization measurements for archeo-and paleo-intensity studies[END_REF]. These application are outside the scope of this study because our focus remains on the archeointensity method initially developed by Le [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF], for which the reliability of the data is essentially verified by linearity criteria (i.e., by the stability of the ratio between the NRM and TRM lab lost fractions as a function of temperature).

Concluding remarks

The archeointensity protocol developed specifically for the Triaxe magnetometer has proven to be effective and reliable, which does not preclude for further improvements, such as the one based on the AutoR'(Ti) data presented in this study. It has already been used in the acquisition of data in Western Europe, the Near East and Central Asia [START_REF] Shaar | Synchronizing geomagnetic field intensity records in the Levant between the 23 rd and 15 th centuries BCE: chronological and methodological implications[END_REF][START_REF] Genevey | Archeomagnetic intensity investigations of French medieval ceramic workshops: Contribution to regional field modeling and archeointensity dating[END_REF][START_REF] Troyano | Analyzing the geomagnetic axial dipole field moment over the historical period from new archeointensity results at Bukhara (Uzbekistan, Central Asia)[END_REF]. The Triaxe archeointensity data obtained in Western Europe, in particular, had led to the novel and intriguing observation of a ~260-year pseudo-periodicity in the occurrence of intensity peaks over the past 1700 years [START_REF] Genevey | New archeointensity data from French Early Medieval pottery production (6th-10th century AD). Tracing 1500 years of geomagnetic field intensity variations in Western Europe[END_REF](Genevey et al., , 2021;;[START_REF] Livermore | Transdimensional inference of archeomagnetic intensity change[END_REF]. These archeointensity studies have so far been the focus of Triaxe magnetometer use and we have not yet further explored the potential of this instrument for more focused studies on rock magnetism, i.e., on the fundamental properties of thermoremanent magnetization in baked clays and volcanic rocks (e.g. [START_REF] Coe | Demise of the rapid-field-change hypothesis at Steens Mountain: The crucial role of continuous thermal demagnetization[END_REF]. For volcanic rocks, the majority of the intensity tests performed to date have been disappointing, as most of the data gathered have been rejected on the basis of the selection criteria established with baked clay artifacts, casting doubt on the predominantly thermoremanent nature of the magnetization carried by many volcanic rocks. With the increasing use of Triaxe-type magnetometers in the paleomagnetism community, there is no doubt that many studies will eventually focus on these aspects. [START_REF] Goff | A new three-axis vibrating sample magnetometer for continuous high-temperature magnetization measurements: applications to paleo-and archeo-intensity determinations[END_REF]. The data are from specimen IR11-02x from Susa, Iran (Shaur Palace of Artaxerxes II; Gallet et al., 2006 and unpublished). The laboratory field for TRM lab acquisition was set at 65 µT, higher than the archeointensity in order to properly discriminate between the M1 and M5 series of measurements in the diagram. In addition, the measurements acquired during the last cooling of the specimen between T2 and room temperature have been omitted. Table 1. Selection criteria used for the interpretation of the Triaxe intensity measurements. Some remarks on these criteria are also provided.

Figure captions

Table 2. Comparisons between the mean archeointensity values obtained at the fragment level and at the fragment-group level for two ensembles of potsherds collected at Tell Begum (BG39-40; [START_REF] Gallet | Tracing the geomagnetic field intensity variations in Upper Mesopotamia during the Pottery Neolithic to improve ceramicbased chronologies[END_REF] and Qatna (group SY03; [START_REF] Livermore | Archeomagnetic intensity variations during the era of geomagnetic spikes in the Levant[END_REF] using two different approaches. An intensity value was obtained at the specimen level by averaging the R'(Ti) data (fourth column). A mean intensity value was derived at the fragment level either by averaging the previous specimen-mean intensity values (fifth column, which is headed "single average (1)") or by considering all combinations of the AutoR'(Ti) values available for the different specimens (sixth column, which is headed "All averages (2)"). The seventh and eighth columns report the mean values determined at the fragment-group level using the means given in the fifth and sixth columns, respectively. The last (ninth) column reports the group mean values obtained from a very large set of combinations of AutoR'(Ti) values from all specimens and fragments within the same group. Table S1. Archeointensity results obtained from the R'(Ti) data shown in Fig. 3,4,5,6,S1 and S2. For the data presented in Fig. 5 and S2 obtained from pseudo-NRM analyses using different cooling rates for TRM lab acquisition, the differences from the applied laboratory field intensity are also given in % (right column). See text and Table 1 for the definition of the different parameters T1'-T2, K, S.

) TB03-03b X -Z -X Z Y -Y Scale: 1e-1 A/m 100°C 500°C 400°C 300°C 200°C -Y -Z Y Z X -X Scale: 1e-2 A/m 100°C 500°C 400°C 300°C 200°C Figure 5 Figure 6 b) Lot31-01 a) Lot31-01c X -Z -X Z Y -Y Scale: 1e-1 A/m 100°C 500°C 400°C 300°C 200°C d) Lot31-04 c) Lot31-04b X -Z -Y Y -X Z 100°C 500°C 400°C 300°C 200°C Scale: 1e-1 A/m f) Lot31-05 e) Lot31-05b X -Z -Y Y -X Z 100°C 500°C 400°C 300°C 200°C Scale: 1e-1 A/m
An archeointensity value at the specimen level is obtained by averaging the R'(Ti) values over the temperature range, between T1' (or T1) and T2, where only the magnetization component acquired during the firing of the analyzed artifact is isolated. These R'(Ti) values must be substantially constant: their evolution as a function of temperature is approximated by a straight line whose slope (referred to as S parameter) must not exceed 10%.

Remark: in practice, we seek as much as possible a temperature interval allowing to obtain a slope of the order of 5%, or less, which remains as close as possible to the temperature range given by the analysis of the thermal demagnetization diagram. The almost constant values of R'(Ti) must be associated with R(Ti) values that progressively increase with temperature. This increase induced by the cooling rate effect on TRM acquisition varies according to the specimens analyzed. Note that in many cases, the R(Ti) values increase only little.

The fraction of magnetization between temperatures T1 (or T1') and T2 must be greater than or equal to 50% of the magnetization remaining at T1 (or T1'). It is referred to as the K parameter. It is estimated after subtracting the effect of the thermal variation of the magnetic fraction remaining blocked at T2 from the magnetization isolated between T1 (or T1') and T2.

Remark: this criterion is different from the one generally used for other paleointensity methods, which implies that an intensity value can only be retained if the magnetization fraction represents more than 50% of the total magnetization carried by the sample. This difference is justified by the large number of magnetization measurements (several dozen) performed between T1 (or T1') and T2 in the Triaxe procedure.

The AutoR'(Ti) values must be substantially constant and close to the R'(Ti) values over a significant temperature range (on the order of 150-250°C) above T1', or even in the most favorable cases over the entire temperature range between T1' and T2' (=T2-~50°C), with the S parameter ideally fluctuating below 10%.

Remark: reducing the selection of accepted data to only those specimens meeting the latter case would lead to the unnecessary rejection of a large number of specimens. On another hand, if the AutoR'(Ti) data are almost constant over the T1'-T2' temperature range, then the R(Ti) data must also be quasi constant, and both datasets must be close to the R'(Ti) data.

The above characteristics established for baked-clay artifacts define a standard magnetic behavior obtained when the magnetic mineralogy of the specimens studied does not alter (or very little) during the thermal treatment. Nevertheless, this absence of significant alteration must be confirmed by magnetic susceptibility measurements during heating: these must be reversible up to temperature T2.

Remark: One of the advantages of the Triaxe protocol is that it minimizes the heating time and the number of heating experienced by the specimen.

  curve. The R'(Ti) and R(Ti) values are thus series of individual data at Ti, while the AutoR'(Ti) values correspond to a series of R'(Ti) averages estimated over temperature intervals beginning at Ti and ending at T2. Several examples for specimens from the Near East are shown in Figures 4 and S1 (see details in the figure captions and Table

Fig. 1 .Fig. 2 .

 12 Fig. 1. Photo of the three Triaxe magnetometers available at IPGP and LAMS. Their builder,

Fig. 3 .

 3 Fig. 3. Thermal demagnetization data obtained for specimen BG39-21c (left panel; Tell

Fig. 4 .

 4 Fig. 4. Thermal demagnetization, R'(Ti), R(Ti) and AutoR'(Ti) data obtained for two

Fig. 5 .

 5 Fig. 5. Triaxe pseudo-NRM analyses of three different baked-clay artifacts. a) a specimen

Figure 1 Figure 2 Curve

 12 Figure1

  Figure S1

  Figure 3 BG39-21 BG39-21c 35 25 320-510 86 1 35.6 ± 0.7 Figure 4 a) IR10-04 IR10-04b 65 25 280-530 92 0 68.4 ± 0.7 b) TB03-03 TB03-03b 35 25 265-515 89 -1 34.4 ± 0.6 Figure 5 a) CHAU01-01 75 25 180-525 87 2 75.2 ± 0.9 (0.3%) 75 10 180-525 87 1 74.7 ± 0.8 (-0.4%) 75 2 180-525 86 0 73.8 ± 0.4 (-1.6%) b) ANG08-05 35 25 180-545 96 1 34.7 ± 0.6 (-0.9%) 35 10 180-545 96 3 34.5 ± 0.6 (-1.4%) 35 2 180-545 96 -6 34.9 ± 1.0 (-0.3%) c) ET13-03 40 25 180-535 95 3 40.7 ± 0.6 (1.8%) 40 10 180-535 95 0 40.6 ± 0.6 (1.5%) 40 2 180-535 95 -2 39.8 ± 0.4 (-0.5%)

Table 1

 1

Table 2

 2

Table S1

 S1

	Figure
	Same specimen
	analyzed
	sequentially
	Same specimen
	analyzed
	sequentially
	Same specimen
	analyzed
	sequentially
	Same specimen
	analyzed
	sequentially
	Same specimen
	analyzed
	sequentially
	Same specimen
	analyzed
	sequentially

Acknowledgements

We thank two anonymous reviewers for their helpful comments on the manuscript.

 [START_REF] Gallet | New Late Neolithic (c. 7000-5000 BC) archeointensity data from Syria. Reconstructing 9000 years of archeomagnetic field intensity variations in the Middle East[END_REF]; and b) Tell Atij [START_REF] Gallet | Analysis of geomagnetic field intensity variations in Mesopotamia during the third millennium BC with archeological implications[END_REF]. The two left panels show the demagnetization diagrams. The right panels exhibit the R(Ti) and R'(Ti) data between T1 and T2 (black filled with grey and blue filled with white dots, respectively), the R'(Ti) data between T1' and T2 (blue dots), and the AutoR'(Ti) data between T1 and T2' (=T2-~50°C) (red dots).

Fig. S2.

Triaxe pseudo-NRM analysis of three additional baked-clay artifacts (see Fig. 5): a) a specimen from Hospices de Beaune, France [START_REF] Genevey | Evidence for rapid geomagnetic field intensity variations in Western Europe over the past 800 years from new archeointensity French data[END_REF]


Sandra O Braz

Diana M Dinca

Geneviève Gourdon

Mário Gomes-Pereira

Real time videomicroscopy and semi-automated analysis of brain cell culture models of trinucleotide repeat expansion diseases

Keywords: Real-time videomicroscopy, primary cell cultures, neuron, astrocyte, oligodendrocyte, trinucleotide repeat expansion, cell phenotypes, semi-automated analysis

Proper brain function requires the coordinated and intricate interaction between neuronal and glial cells. Like many other neurological conditions, trinucleotide repeat expansion disorders are likely initiated by the synergistic combination of abnormalities hitting different brain cell types, which ultimately disrupt brain function and lead to the onset of neurological symptoms. Understanding how trinucleotide repeat expansions affect the phenotypes and physiology of neurons and glia is fundamental to improve our understanding of disease mechanisms in the brain and shape the design of future therapeutic interventions.

Here we describe a protocol for semi-automated videomicroscopy analysis of cultured brain cells, maintained under suitable and controlled conditions. Through real-time monitoring of basic cell phenotypes (such as proliferation, cell morphology, differentiation and migration) this method provides an accurate primary assessment of the impact of the repeat expansion on the physiology of neurons and glia. The versatility of the system, the automated image acquisition and the semiautomated processing of the data collected allow rapid phenotypic analysis of individual cell types, as well as the investigation of cell-cell interactions. The stability of the acquisition system provides reproducible and robust results. The raw data can be easily exported to other software to perform more sophisticated imaging analysis and statistical tests. In summary, the methods described offer versatile, reproducible and time-effective means to dissect the impact of the repeat expansion on different brain cell types and on intercellular interactions.

Introduction

Trinucleotide repeat expansion diseases comprise neuromuscular, neurodevelopmental and neurodegenerative disorders [START_REF] Spada | Repeat expansion disease: progress and puzzles in disease pathogenesis[END_REF][START_REF] Budworth | A brief history of triplet repeat diseases[END_REF], which are usually associated with cognitive deficits and behavioral changes. Today we know that repeat expansions mediate disease etiology through a variety of mechanisms, including loss of function (e.g. fragile X syndrome, FXS; and Friedreich ataxia, FRDA) [START_REF] Wang | New perspectives on the biology of fragile X syndrome[END_REF][START_REF] Martelli | Understanding the genetic and molecular pathogenesis of Friedreich's ataxia through animal and cellular models[END_REF], toxic gain of function of polyglutamine tracts (e.g. Huntington disease, HD) [START_REF] Ross | Huntington's disease: from molecular pathogenesis to clinical treatment[END_REF] and RNA toxicity (e.g. myotonic dystrophy type 1, DM1) [START_REF] Sicot | Myotonic dystrophy, when simple repeats reveal complex pathogenic entities: new findings and future challenges[END_REF]. On top of these mechanisms, the accumulation of toxic homopolypeptides by repeat-associated non-AUG (RAN) translation may also play a determinant pathogenic role [START_REF] Cleary | Repeat-associated non-ATG (RAN) translation in neurological disease[END_REF].

Mouse models have been crucial to decipher the molecular pathogenesis of trinucleotide repeat expansion diseases, notably in the brain, where they recreate the intricate and multidirectional interactions between different cell types [START_REF] Braz | Of Mice and Men: Advances in the Understanding of Neuromuscular Aspects of Myotonic Dystrophy[END_REF]. However, the broad view revealed by the analysis of tissue samples, does not grant sufficient resolution to focus on pathological events occurring in specific cell populations. This is particularly relevant in conditions in which it is conceivable that disease susceptibility and the cascade of pathogenic events vary between different cell types and/or subpopulations. Cell type selective pathogenesis in trinucleotide repeat expansion disorders is illustrated by the preferential degeneration of medium spiny neurons in the striatum of HD patients [START_REF] Rikani | The mechanism of degeneration of striatal neuronal subtypes in Huntington disease[END_REF] and by the pervasive loss of Purkinje cells in many spirocerebellar ataxias (SCAs) [START_REF] Huang | Why do so many genetic insults lead to Purkinje Cell degeneration and spinocerebellar ataxia?[END_REF], while other neuronal types remain relatively spared. Rat primary cultures corroborated the increased vulnerability of striatal neurons to expanded huntingtin, relative to cortical neurons [START_REF] Zala | Progressive and selective striatal degeneration in primary neuronal cultures using lentiviral vector coding for a mutant huntingtin fragment[END_REF].

More recently, the generation of human iPSC-derived Pukinje cells confirmed the burden of SCAs to this neuronal cell type [START_REF] Wong | Recent advances in modelling of cerebellar ataxia using induced pluripotent stem cells[END_REF].

In spite of the neuronal damage that precedes the neurological manifestations of trinucleotide repeat expansion diseases, a less "neurocentric" view of brain pathology is emerging, pointing to the key role of glial cells. It has been reported that astrocyte dysfunction prevents normal neuronal development and proper synaptogenesis in FXS [START_REF] Jacobs | Astrocytes prevent abnormal neuronal development in the fragile x mouse[END_REF][START_REF] Jacobs | Hippocampal neuronal subtypes develop abnormal dendritic arbors in the presence of Fragile X astrocytes[END_REF] and FRDA [START_REF] Franco | A role for astrocytes in cerebellar deficits in frataxin deficiency: Protection by insulin-like growth factor I[END_REF][START_REF] Loria | Frataxin knockdown in human astrocytes triggers cell death and the release of factors that cause neuronal toxicity[END_REF].

Astrocyte deficits may also cause Purkinje cell hyperexcitability in SCA7 [START_REF] Custer | Bergmann glia expression of polyglutamine-expanded ataxin-7 produces neurodegeneration by impairing glutamate transport[END_REF] and DM1 [START_REF] Sicot | Downregulation of the Glial GLT1 Glutamate Transporter and Purkinje Cell Dysfunction in a Mouse Model of Myotonic Dystrophy[END_REF]. Signs of astrocyte pathology were also reported in HD [START_REF] Khakh | Astrocytes and Huntington's disease[END_REF], fragile X-associated tremor/ataxia syndrome (FXTAS) [START_REF] Wenzel | Ubiquitin-positive intranuclear inclusions in neuronal and glial cells in a mouse model of the fragile X premutation[END_REF] and SCA1 [START_REF] Cvetanovic | Early activation of microglia and astrocytes in mouse models of spinocerebellar ataxia type 1[END_REF]. In addition to astrocytes, oligodendrocytes are also affected by trinucleotide repeat expansions. The dysfunction of this glial cell type was documented in mouse models of HD and FXS, and it was characterized by delayed and reduced myelination in association with defective maturation of oligodendrocytes [START_REF] Jin | Early white matter abnormalities, progressive brain pathology and motor deficits in a novel knock-in mouse model of Huntington's disease[END_REF][START_REF] Pacey | Delayed myelination in a mouse model of fragile X syndrome[END_REF]. The involvement of microglia in trinucleotide repeat expansion diseases has been less explored. Nonetheless, microglia activation was reported in HD patients, as well as in mouse models of HD [START_REF] Tai | Imaging microglial activation in Huntington's disease[END_REF][START_REF] Franciosi | Age-dependent neurovascular abnormalities and altered microglial morphology in the YAC128 mouse model of Huntington disease[END_REF] and SCA1 [START_REF] Cvetanovic | Early activation of microglia and astrocytes in mouse models of spinocerebellar ataxia type 1[END_REF]. In HD models, microglia activation is accompanied by abnormal cell morphology and defective migration response to chemotaxis [START_REF] Kwan | Mutant huntingtin impairs immune cell migration in Huntington disease[END_REF].

Together these findings demonstrate the diverse response of different brain cells to disease, and the critical insight that can be gained from the analysis of individual cell types. Understanding the disease mechanisms that operate in different cell populations and underlie the complex neurological symptoms is crucial for future therapeutic developments. Cell culture techniques provide a powerful setting to address this question. While homogenous cell cultures can reveal the impact of the disease on the phenotype and physiology of different cell types, mixed cultures (established through the combination of different cells) can help untangle the mechanisms of intercellular miscommunication in the brain and their contribution to disease. These studies were traditionally performed on cultured cells fixed at different time points. Although informative, this strategy presents two main limitations: (a) the readouts represent isolated snapshots of physiological processes that are continuously ongoing and they may therefore miss relevant events; (b) the methodology is time-consuming, involving cell manipulation, cell labeling, signal detection, data collection and laborious data analysis.

The alternative is to perform prolonged recordings of live cell phenotypes, by real-time semi-automated videomicroscopy. The success of this methodology depends on the maintenance of well-defined and suitable culture conditions, which must guarantee cell function and stability over long periods of time. This requirement is particularly important for brain cells, given their high sensitivity to environmental changes: subtle uncontrolled fluctuations may have deleterious consequences to cell homeostasis and affect the quality and reproducibility of the results. The advanced Incucyte® live-cell analysis system (Essen Bioscience) reduces these technical challenges and maximizes the quality and reproducibility of the data collected. The videosystem is incorporated in a regular CO2 incubator, allowing the cells to be kept in their optimal temperature and humidity throughout the experiment. Image acquisition is automatic: once the experimenter has defined the acquisition settings he/she can walk away, leaving the cells unperturbed. The semiautomatic analysis of the Incucyte® system is another major strength: the system offers pre-defined analysis settings that can be adapted by the scientist to better fit the type of cells and the biological process being studied. Once defined, the same settings and analysis can be immediately applied to the images as they are acquired by the video system -the computerized workflow translates into real-time results and traceability of eventual problems, therefore saving precious time.

The methods described here provide a videomicroscopy semi-automated approach to assess the primary impact of the disease on different cell lineages of the brain (neurons, astrocytes and oligodendrocytes), using proliferation, differentiation, cell adhesion, morphological complexity and migration (among others) as basic readouts of cell dysfunction. The results generated offer meaningful clues to design future experiments and extend the analysis of the disease mechanisms operating in individual cell populations, through more refined approaches. In spite of our focus on brain cells, the protocols can be easily adapted and applied to a myriad of other cell types. 8. Equipment maintenance: 0.5% Alconox, 1% Virkon, double distilled H2O and 70% Ethanol.

Materials

Specific equipment and reagents

Mouse experiments

For experiments involving animal models you must follow the institutional, national and/or international guidelines for ethical conduct in the care and use of animals.

Cell culture media and coating reagents

1. Dissection medium for neurons and astrocytes: Leibovitz's L-15 Medium, 30 µM glucose.

2. Dissection medium for OPC: Hank's balanced salt solution without (HBSS) Ca 2+ and Mg 2+ .

Neuronal medium:

Neurobasal-A, 1X B27 supplement, 0.5 mM L-Glutamine, 0.25 µg/mL Amphotericin B, 100 U/mL penicillin and 100 µg/mL streptomycin.

4. Astrocytes medium: DMEM low glucose, 10% fetal bovine serum (FBS) and 0.05 mg/mL Gentamycin.

5. OPC medium: DMEM F-12, 1% N2 supplement, 2% B27 supplement, 50 units/mL penicillin and 50 µg/mL streptomycin, 0.01% bovine serum albumin, 40 ng/mL FGF2 and 20 ng/mL PDGF-AA.

6. Oligodendrocytes medium: DMEM F-12, 1% N2 supplement, 2% B27 supplement, 50 units/mL penicillin and 50 µg/mL streptomycin, 50 μg/mL insulin, 40 ng/mL triiodothyronin and 1 ng/mL CNTF.

7. Coating: 0.1 mg/mL of poly-D-lysine (PDL) and 10 µg/mL laminin (See Note 1).

Methods

Primary mouse cell cultures

Different protocols are available in the literature and can be used to establish primary mouse neurons, astrocytes and oligodendrocytes cultures. We briefly describe below the protocols used in our laboratory to ensure healthy and reproducible cell cultures prior to cell seeding (see Note 2).

For primary neuron cultures:

1. Prepare primary dissociated cell cultures of cortical neurons from embryonic day (E) 16.5 mouse embryos (see Note 3). Dissect the brain and place it on a dish with dissociation medium for neurons, under a stereoscope.

2. Separate the cortices from the rest of the brain and carefully remove the meninges with the help of micro tweezers.

3. Maintain the cortices in dissection medium on ice until end of the dissection.

4. Wash the cortices twice with neuronal medium, previously warmed at 37˚C. 5. Incubate the cortices in neuronal medium containing 0.05% of trypsin and 10 U/mL of DNaseI 15 min at 37ºC, shaking gently the tube every 5 minutes (min).

6. Wash the cell suspension twice with neuronal medium supplemented with 5% FBS to inhibit further tissue digestion by trypsin.

7. Dissociate the cells passing them through a 21 G needle, wash twice in neuronal medium, collecting the cells at 200 g for 5 min at room temperature (RT) in between washes. Note 4) before plating at appropriated cell density in PDL and laminincoated 96 well plates (see Note 5) in pre-warmed neuronal medium containing 5% FBS. 9. Replace the medium 4 hours (h) after plating with neuronal medium supplemented with 100 μM AraC and withdraw the FBS to avoid astrocytes proliferation (see Note 6).

Count cells (see

10. Change half of the medium every 3-4 days (see Note 7).

For primary astrocyte cultures:

1. Prepare primary dissociated cell cultures of cortical astrocytes from postnatal day (P) 1 pups (see Note 3). Dissect the cortices as described above.

2. Wash the tissue twice with 1 mL of astrocytes medium and mechanically dissociate, passing through 21 G needle twice and then three times through a 26 G needle, waiting 2-3 min for the sedimentation of the fragments in between each dissociation.

3. Centrifuge the cell suspension 200 g for 5 min at RT, remove the supernatant and resuspend the cells in 10 mL of pre-warmed astrocytes medium.

4. Plate the cells in a T75 dish at 300,000 cells/mL. 5. On the following day, change the medium (see Note 7) to remove the dead cells.

6. Primary astrocytes adhere, grow and proliferate on both coated and uncoated dishes (see Note 1 and 6).

7. Incubate the cells for 2 weeks in astrocyte medium, changing the medium every 3 days (see

Note 8).

For primary oligodendrocyte progenitors (OPC) cell cultures:

1. Prepare dissociated cell cultures of cortical OPC from P0-P2 mice (see Note 3). Perform the dissection as described above using dissection medium for OPC.

2. Dissociate the tissue using the neural tissue dissociation kit and following the manufacture's protocol. Dissociate the tissue by cycles of mechanic and enzymatic dissociation to produce a single cell suspension, then filter the suspension through a 70 µm strainer and wash with HBSS with Ca 2+ and Mg 2+ .

3. Centrifuge the cell suspension at 300 g for 10 min at RT, remove the supernatant and resuspend the cells in a 1X PBS with Ca 2+ and Mg 2+ , supplemented with 5% BSA (BSA buffer).

4. Isolate the OPC using magnetic anti-CD140a (PDGFRα) labelled microbeads (protocol adapted from Dincman et al. 2012 [29]). 5. After blocking, incubate the cells with the magnetic microbeads to allow the binding of the PDGFRα + cells, then wash the cells in BSA buffer and collect at 300 g for 10 min at RT, to eliminate cell-free microbeads. 6. To separate the OPC, pass the cell suspension trough a column attached to a magnetic field, where the PDGFRα + cells are retained.

7. Remove the column from the magnetic field and flush the cells in OPC medium.

8. Count OPC and plate them at a density of 9,000-15,000 cells/cm 2 in PDL and laminincoated 96 well plates (see Note 5) and keep in OPC medium to proliferate (See Note 9). 9. Two days later, replace the medium with oligodendrocyte medium, in order to induce the differentiation of OPC into oligodendrocytes (see Note 6).

Cell proliferation (Astrocytes and OPC)

In this protocol, cell proliferation is estimated by the percentage of well surface area occupied by cells (i.e. confluence) over time.

1. Seed the astrocytes and OPC at 20,000 or 5,000 cells/well, respectively (see Note 10), on 96-well plates (see Note 5) and keep the cells in their corresponding culture media.

2. Set the system to acquire a phase-contrast picture (see Note 11) every 45 min over 50 h for astrocytes, or every 2 h during 7-8 days for oligodendrocytes, selecting the "Standard" type of scan and using the 20X objective (see Note 12). Change the cell medium every 2-3 days (see Notes 6 and 13). 4. Using this collection of images, define the settings to be applied to the analyses. Under the option "New Processing Definition" (Fig. 1 option B) run the "Preview Current" command to automatically generate the "Confluence Mask" that will contour and cover the surface of what the software considers to be cells in the first picture selected.

5. Since the automatic mask is unlikely to fit all the cell shapes/densities through time and/or all cell conditions (Fig. 2A), adjust this mask in order to fully outline the shape of the cells.

To do so use the filters named "Cell/Background", "Size" and "Eccentricity", which are available on the "Segmentation Adjustment" panel (See Note 16).

6. Apply the defined settings to all the image collection by selecting the option "Preview All", and check if the mask fits perfectly the majority of the cells in the different images of the image collection previously chosen (Fig. 2B).

7. Initiate the analysis by selecting "Launch New Analysis Job" (Fig. 1 option C). The software will apply the defined settings to all the pictures acquired from the plate over time (see Note 17).

Wound healing migration (Astrocytes and OPC)

This analysis evaluates the capacity on the cells to migrate towards and fill an empty space created by a wound on a cell monolayer. It estimates the percentage of the wound surface occupied by migrating cells over time. 3. Set the system to acquire a phase-contrast picture (see Note 11) every 45-60 min over 3 days (See Note 22), selecting the "Scratch Wound" on the "Scan Type" dropdown and using the 10x objective. [START_REF] Martelli | Understanding the genetic and molecular pathogenesis of Friedreich's ataxia through animal and cellular models[END_REF]. Set the image analysis by creating a representative set of images taking into account the phenotype to be analyzed over time (See Note 14). To this end, select the first picture and click on "Create or Add to Image Collection" (Fig. 1 option A), select the "Scratch Wound" option on the job type mode and add the image to the new image collection. Select another 4-6 new images (see Note 15), including the first scan (time 0) after wounding, which will be used to define the initial mask for the surface of the wounded (cell-free) area. Add the images to the previous image collection. [START_REF] Ross | Huntington's disease: from molecular pathogenesis to clinical treatment[END_REF]. Using this collection of images, define the settings to be applied to the analyses. Under the option "Process Definition" (Fig. 1 option B) run the "Preview Current" command on the first picture to automatically generate the "Confluence Mask" (which will cover and contour the surface of what the software supposes to be a cell layer (Fig. 3A-1)) and the "Scratch Wound Mask" (which will cover the cell-free space created by the scratch (Fig. 3A-3)).

6. Since the automatic mask may not be able to perfectly detect and outlines the cell layer limits and/or some cell debris may remain on the wounded area (Fig. 3A), adjust the mask so it fully covers and delineates cell shapes and the cell front on each side of the wound, using the filters named "Cell/Background", "Size" and "Eccentricity" available on the "Segmentation Adjustment" panel (See Notes 16 and 26).

7. Apply the defined settings to the entire image collection previously created by running "Preview All" and check if the masks provide a perfect fit for the front of migration (Fig. 3A-2), cell layer and wounded space for the majority of the different images previously selected (Fig. 3B).

8. Initiate the analysis by clicking on the "Launch New Analysis Job" (Fig. 1 option C). The software will apply the settings previously defined to all the pictures acquired on the plate (see Note 17). 9. The results will estimate the rate of occupancy of the space left empty following the scratch.

They can be represented by one of three measurements: a) wound width over time (distance between the front of migration of each side of the cell-free space); b) wound confluence over time (the percentage of the wound initial surface occupied by the cells) or c) relative wound density (RWD, the percentage of the cell density in the wound relative to the percentage of cell density in the regions surrounding the wound) (see Notes 21 and 27) (Fig. 3C).

Single cell tracking (Astrocytes)

This analysis allows the drawing of the migration path, the measurement of speed, directionality and displacement of individual astrocytes by manually tracking the position of the nucleus in each image, using Fiji and the open-source DiPer computer software [START_REF] Gorelik | Quantitative and unbiased analysis of directional persistence in cell migration[END_REF].

1. Following the wound healing assay, export migration videos from the IncuCyte® in uncompressed AVI format and open them using Fiji (see Note 28). Make sure to export at least one video (or one image) with the embedded scale, to be able to use it for setting the scale in Fiji (see Note 29). The image visualization of the videos can be modified to facilitate tracking (See Note 30 and 31).

2. After setting the scale (see Note 28) use the "Manual Tracking Fiji" plugin to track the nucleus of at least 40 astrocytes per video. Insert the time interval in the corresponding panel of the Tracking window, and the x/y calibration (corresponding to the pixel width, which can be found when setting the scale in Fiji). Start tracking by clicking "Add track" (Fig. 4A) and following the advancement of one selected cell. This will measure the position of the astrocytes on the X and Y coordinates, the distance and the velocity of the astrocytes (Fig. 4B).

3. The distance and velocity results can be directly analyzed using the appropriate statistical program, or they can be used to calculate more complex parameters using the DiPer opensource macros for Microsoft Excel [START_REF] Gorelik | Quantitative and unbiased analysis of directional persistence in cell migration[END_REF]. These macros allow the calculation of cells directionality (defined as the straight line between the start and the end point of the migration trajectory divided by the total length of the trajectory), or the mean square displacement (which will assess the area explored by the migrating cells over time), as well as plotting each cell trajectory, individually or collectively, on X/Y axes, emanating from the original starting point of displacement (Fig. 4C).

Morphological complexity (Neurons and Oligodendrocytes)

In the following protocols the analyses measure the changes in length and branching of neurites and oligodendrocyte processes over time.

Neurite development:

1. Seed primary neurons at 30,000 cell/well on a coated 96-well plate and keep the cells in neuronal medium (see Notes 1, 5 and 10). Incubate the plates at 37ºC overnight before placing them in the Incucyte® 16 hours later (in the next morning).

2. Set the system to acquire one phase-contrast picture (see Note 11) every 3 h over 7 days, using the "Standard" type of scan and the 20X objective, (see Notes 12 and 32).

3. Set the image analysis, starting by creating a representative set of images taking into account the cell morphology to be analyzed during the course of the experiment (see Note 14). To this end, select the first picture and click on "Create or Add to Image Collection" (Fig. 1 option A), select the mode "Neurotrack" as job type and add the image to the new image collection. Select another 4-6 new images (see Note 15) and add them to the previous collection.

4. Using this collection of images, define the settings to be applied to the analyses. Under the option "Process Definition" (Fig. 1 option B) run the "Preview Current" on the first picture to automatically generate the "Phase Cell-Body Cluster Mask" and a the "Phase Neurite Mask" (See Note 33). The first will cover and contour only the cell-body surface, while the second will trace what the software assumes to be neurites (cell processes).

5. Due to the complexity of the neuronal cultures, the automatic mask may not be able to perfectly trace all neurites. In that case, select "Phase Neurites (Analyze)" and adjust the masks in order to fully cover and delineate the cell shape. To this end, use the filters "Cell/Background", "Size" and "Eccentricity" to adjust the "Phase Cell-Body Cluster Mask" (see Note 16), and the neurite sensitivity parameters (see Note 34) for the "Neurite Mask" refinement. These option and filters are available on the "Cell-body Cluster" and "Neurite" parameter panels.

6. Apply the defined settings to all the image collection by running "Preview All" and check if the masks faithfully reproduce the complexity of the neuronal cell shape (Fig. 5A).

7. Initiate the analysis selecting the "Launch New Analysis Job" (Fig. 1 option C) and select the "NeuroTrack" job type. The software will apply the settings previously defined to all the pictures acquired on the plate throughout the experiments (see Note 17).

8. The results will measure the changes in neurite length and number of branching points per well surface over time (Fig. 5C) (see Note 21 and 35).

Process extension:

1. Seed OPC at 5,000 cell/well on coated 96-well plates and keep the cells in OPC medium for two days to stabilize the culture (see Notes 1, 5 and 10).

2. Set the system to acquire one phase-contrast picture every 2 h over 8-10 days, selecting the "Standard" type of scan and using the 20X objective (see Notes 12 and 32). 4. To set the analysis, start by creating a representative set of images taking into account the changes in cell morphology that occur over time and that you will monitor over the course of the experiment (See Note 14). Proceed as described in section 3.5 (Neurite development, steps 4-5). Please note that the "Neurotrack" algorithm was developed to analyze neurons, so the automatic mask may not be able to perfectly detect the thin oligodendrocytes processes and secondary branching (See Note 34). To adjust the "Neurite Mask" to better fit oligodendrocyte processes and branching, set the "Neurite" parameters to maximum sensitivity (See Note 36) (Fig. 6A).

5. Launch the analyses as described in section 3.5 (Neurite development, step 7).

6. The results will show the length of the processes and the number of secondary branching per cell-body cluster, thus reflecting the cell differentiation over time (see Note 21 and 35) (Fig. 6C and6D).

Co-cultures analyses:

This type of analysis allows the investigation of neuro-glia interaction. In particular, this protocol describes how to assess the effect of astrocytes on neurite development by measuring the neurite length and branching of red fluorescent neurons over time, in the presence of astrocytes.

1. Seed primary neurons at 30,000 cell/well on a coated 96-well plate and keep the cells in neuronal medium supplemented with 5% of FBS (see Notes 1, 5 and 10). Incubate cells at 37ºC for 4 h. 4. Place the plate on the Incucyte® imaging system and set the scan to acquire one image every 3 h with the 20X objective for 10 days, selecting both phase contrast and red fluorescence channels and using the "Standard" type of scan (see Notes 10 and 12). 5. Two days after the beginning of the acquisition (when the Neurolight red signal in the neurites is sufficiently bright) optimize the "z" focus of the Incucyte ® objective manually, by moving its scale ring (usually 1.2 it is a good starting point). Test the "z" focus by clicking on "Scan on Demand" and scan some wells of the plate each time you move the ring, until you get a good image resolution of the red neurites across the entire plate.

6. Set the image analysis. Start by creating a representative set of red fluorescent images taking into account the cell morphology and cell structures to be monitored over time (see Note 14). To this end, select the first picture and click on "Create an Image Collection" (Fig. 1 option A), select the mode "Neurotrack" as job type and add the image to the new image collection. Select another 4-6 new images (see Note 15) and add them to the previous collection.

7. Using this collection of images, define the settings to be applied to the analyses. Under the option "Process Definition" (Fig. 1 option B) run the "Preview Current" on the first picture to automatically generate the "Cell-Body Cluster Mask" and a the "Color Neurites Mask". The first will cover and contour only the cell-body surface, while the second will trace what the software presumes to be neurites.

If the automatic mask is not able to perfectly trace all neurites, adjust it in the "Color

Neurites (Analyze)" panel in order to fully cover and delineate the cell shape using the filters "Cell-Body Cluster Parameters", "Cleanup", "Cell-Body Cluster Filters" and "Neurite Parameters" for the "Color Neurites Mask" (see Note 34) (Fig. 7A). 9. Launch the analysis as described in section 3.5 (Neurite development, steps 6-7).

Notes

1. Different extracellular matrix coating protocols and reagents can be used according with the experiment (e.g. to study cell-to-matrix interactions) and cell type used. First, coat plates with PDL for at least 20 min at room temperature and wash three times with double distilled sterile H2O. Second, coat plates with laminin at least 3h at 37ºC and wash the plates twice with 1X PBS.

2.

The described cell culture protocols can be used to culture cells from either one single embryo/pup or pooled animals.

3.

To obtain mouse embryos at E16.5 plan the mice breeding in advance. For efficient mouse mating, we recommend to cross in the same cage one male with one/two females in proestrus/estrus phase in the evening, and check for vaginal plugs in the morning of the next day. Since the coupling is not 100% efficient you may want to scale up the number of breeding couples. Also some strains/transgenic mice have lower breeding rates, which decrease the number of embryos. For the P0-P2 mice pups, one male and two females were maintained continuously in the same cage.

4.

For the cell counting dilute the cells in trypan blue and count them using a neubauer chamber. Other counting methods/instruments can also be used.

5.

For the protocols described, in our laboratory we use 96 well plates (unless stated otherwise), since this type of microplate allows the use of a lower cell number and volume of reagents and a high number of biological and experimental replicates per plate.

However the Incucyte® imaging system supports a high variety of common cell vessels (listed in the manufacturer datasheets) that can be used accordingly with the cell type/experiment need. In addition the system can accommodate and read six microplates simultaneously, allowing high throughput processing of samples and easy reproducibility of the results.

6.

The purity of the primary cultures can be verified by immunostaining detection of cell type-specific markers, such as NeuN for neurons, GFAP for astrocytes and Olig2 for oligodendrocytes.

7.

Medium should be changed with fresh pre-warmed medium at 37ºC. Cell culture medium should be replaced carefully avoiding to touch or disturb the cell layer at the bottom of the plate.

8.

In most experiments described experiments we used astrocytes cultured on uncoated plates during 2 weeks in vitro, unless stated otherwise.

9.

The cell density is a critical issue for the OPC culture: suboptimal densities can lead to cell death (if cell density is too low) or undesired cell differentiation (if cell density is too high). The OPC and oligodendrocytes are loosely attached to the surface of the dishes, for this reason medium changes should be performed with extra care, using pre-warmed medium and gentle handling of the plates.

10. The cell densities indicated are suitable for the cell types mentioned in the protocol.

However for other cell types, optimal seeding densities should be established prior to the experiment. After seeding, incubate the plates 15 min at room temperature to allow the cells to distribute evenly across the bottom of the plate, before placing the cells in the Incucyte® incubator. Wait 15 min before starting the scan, to allow the dissipation of eventual condensation accumulated on the plate lid.

11.

In addition to the acquisition of images in a stain-free manner, the system is also able to detect fluorescence, which diversifies the array of possible experiments. Notably, the fluorescent labeling of nuclear DNA is an alternative method to precisely count the number of cells and monitor cell proliferation, or the fluorescent labeling of neurites for the length and branching measurements (as described in section 3.4).

12. To avoid variability resulting from scanned images collected from non-representative areas of the well (in case of uneven cell distribution), it is recommended to select multiple pictures per well by editing the "Scan Pattern".

13.

To change the cell medium while the experiment is ongoing, remove the plate from the machine in between two scans, change the medium and reintroduce the plate in the machine before the next scan begins.

14.

The aim of creating an image collection is to have a set of representative images to train and test the settings that will be subsequently used for the analysis.

15.

Although the number of pictures in the image collection is not limited, adding too many pictures slows down the process of setting definition. The selected images should represent all the possible cell variations in shape, density or other relevant parameters over time and/or across all the conditions tested. Pictures with bizarre cell density or poor image quality should not be included in the image collection and they should be excluded from later data analyses. For example, pictures taken close to the border of the well may show non-homogeneous cell density, due to uneven seeding.

16.

The "cells/background" sliding cursor is useful to distinguish more or less cells from the background. If the culture is clean from cell debris and/or the cells are flat/low contrasted, the cursors should be shifted towards the cell side to filter in the entire cell. In contrast, if the culture contains cell debris and the cell have a good phase-contrast, the cursor should be placed towards the background. Additionally, as the cell debris is normally round and of smaller size than cells, it can be filtered out by the "size" and "shape" filters -increasing the minimum "size" and "eccentricity" of the object to be considered as cells.

17.

For the same group of acquisitions ("Vessel Scans") we can generate as many image collections and process definitions as we feel necessary to better adapt the analysis to the biological process being studied. Those can be applied to all the scans at any time of the experiment while they are acquired by the imaging system,, using the option "Open Ended". The good fitting of the mask can be checked for each individual image once the analysis is launched and finished.

18.

Using the "Cell Confluence Mask" as a measurement on cells with high cytoplasm spreading and low contrast can be difficult, because different biological processes, such as cell adhesion/spreading and proliferation, cannot be easily distinguished. To overcome this potential problem cell adhesion/spreading can be discriminated from proliferation by focusing the analysis on the initial 45 min after cell seeding. Additionally it is possible to export single images from the Incucyte® software and manually analyse the cell surface with another software (e.g. freehand selection tool available in ImageJ or FIJI).

19. This is a limitation common to other methods to measure cell growth, such as the impedance-based monitoring system xCELLigence® RTCA MP system (ACEA Biosciences Inc). Although the xCELLigence® is able to detect in real-time very fast changes in cell index (based on the measurement of electrical impedance caused by the cell growth and attachment of the cells to the plate), the results are more difficult to interpret due to the lack of visual readout.

20.

The software also counts the number of "objects" (cells) over time, as a measurement of cell proliferation. However, using phase-contrast pictures for this measurement can be misleading. As a result of proliferation, the cells become, naturally more confluent, and the individual cells previously identified as isolated "objects" become adjacent and make physical contact. They are no longer distinguished, as two distinct objects, but as a single one. Consequently the "cell count" provided by the software is underestimated, and it may even decrease over time, while the real cell number on the plate goes up. A proposed solution to overcome this problem is the use of fluorescent live-cell nuclear markers.

21.

The results can also be illustrated by an overview of the global population dynamics in the entire place, and individual plots can be drawn for each well of the microplate. They can also be exported as a numerical data file for subsequent statistical treatment and graphical representation by specialized software.

22.

For astrocytes, uncoated plates work well and the wound takes about 50 h to close. However, the coating accelerates cell adhesion and migration, and it can help studying interactions between the cells and the extracellular matrix. For OPC the coating is necessary for cell adhesion. Although single coating with PDL can be used, allowing the wound to close over 72 h, PDL+laminin coating increases the speed of wound healing, which is completed after 48 h. Hence, the duration of the experiment will depend on the type of coating used.

23.

The use of "Incucyte ® Image lock" special plate is crucial to assure that all the scans will be performed at exactly the same position throughout the experiment, since each well is marked with a cross that the IncuCyte® will use as a reference point for the positioning of the picture within the well. This will avoid variability in the scan position and therefore in the analysis.

24.

Remember to clean the Incucyte® wound maker before using it with successive 5 min washes with 45 mL of double distilled H2O and 45 mL of 70% Ethanol, using the reservoirs from the kit. While wounding the plates, all wells of the plate should contain 100 µL of medium or PBS 1X. After finishing wounding your plates, clean again the wound maker with successive 5 min washes of 45 mL of 0.5% Alconox, 1% Virkon, double distilled H2O and 70% Ethanol. To maintain the good working conditions of the instrument it's extremely important to never turn the pin block of Incucyte® wound maker upside down, or the solutions/media will crystalize inside the pins and impair the scratching.

25.

After the wound is made, lift the pin block straight up to avoid damaging the front of migration. This step will create a precise and reproducible wound throughout the 96-well plate. Using the wound maker instead of the classical pipette tip presents a great advantage since it maximises the homogeneity of the wound between wells, it guarantees the regularity of the fronts of migration and it reduces cell lifting. Overall the process is time saving: the 96 wells of a plate can be scratched in parallel in one single passage of the wound maker.

26.

Changes the filter parameters in the "Segmentation Adjustment" panel will change both "Confluence Mask" and "Scratch Wound Mask". It is not possible to apply different parameters to the different masks.

27.

Although this system, like the classical wound healing assays [START_REF] Molinie | Directional Collective Migration in Wound Healing Assays[END_REF], does not avoid the damage-induced cell proliferation, the analysis with RWD algorithm automatically normalizes the confluence in the wound space to the confluence in the cell layer, excluding the contribution of factors other than migration (e.g. proliferation or cell death) towards the filling of the wound.

28.

To export the videos from the IncuCyte® go to "Utilities>Export Movie or Image set" and then select the right time range (from the beginning of the experiment until the moment when the wound is closed for all conditions) and the right wells. At least one video should have the scale ("Legend") and Timestamp should be included in all videos, as a reminder for further analysis. Use "Windows AVI video (uncompressed)" format to ensure compatibility with Fiji.

29.

Set the scale in Fiji by using the line tool to measure the scale on one Incucyte® image and then select "Set Scale" in the Fiji "Analyze" menu. Remember to check the "Global" box in order to apply the scale to all further images.

30.

To better visualize the nucleus of the cells the stack can be flipped on the Z scale (time scale) to start the analysis from the end of the video, when the cell cytoplasm is widely spread. Once delineated, the tracking position data can be flipped back using Microsoft Excel.

31.

The color of the migration images can also be processed to facilitate the visual detection of the nucleus. We recommend converting the images to 8-bit and applying the "HiLo LUT" palette.

32.

Depending on the experiment the acquisition can be done over different time periods.

33.

If you label neurites fluorescently, use the "Color Cell-Body Cluster Mask" and the "Color Neurite Mask" to define the settings for analysis.

34.

Use the "Filtering" parameter to filter out small imperfections (e.g. due to irregular coating) or cell debris that can be mislabelled as neurites. This parameter can also be used to filter in thinner neurites/cell processes, through the selection of the "Better" or "Best" options in addition to the "Neurite Sensivity" slider. Define the pixel size at which the cell bodies transit into neurites/cell processes using the "Neurite Width" dropdown.

35.

The neurite length and branching results can be quantified relative to the area of the well, to the cell-body cluster number or area. For neurons, the quantification of the neurite length per area is normally used, when the physiological neurite dynamics is being tested, since the number of neurons and cell body area will not change over time. However, when compounds are tested, the normalizations to cell body should be considered to exclude confounding effects, such as cell death. For oligodendrocytes, it is more accurate to normalize the measurements of cell processes relative to the cell body clusters, in an attempt to minimise the influence of possible alterations in cell number. The cell-body dynamics is estimated by measuring the percentage of confluence. 36. We adapt the "Neurotrack" algorithm to measure the process extension and branching of oligodendrocytes. However neurites and axons are bigger and thicker than the oligodendrocytes processes. As a result the settings must be adjusted to the highest sensitivity to maximise the detection of truly thin oligodendrocytes processes and branching, while distinguishing them from cell bodies.

37.

A MOI of 3 TU/cell was optimized in our laboratory to infect primary cortical neurons.

For other neuronal types the optimal MOI must be determined.

38.

The described protocol is useful to test, for instance, the beneficial/deleterious effect of an astrocyte monolayer of a given phenotype on neuronal differentiation, and the response to environmental stress, such as the addition of a neurotoxic compound (e.g. excess of glutamate). The process starts with the creation of an image collection (option A), followed by the definition of the analysis settings that will be applied to all the images subsequently acquired (option B). Finally the analysis in launched (option C).

6 .

 6 GentleMACS Dissociator (130-093-235 or superior, Miltenyi). 7. Neural tissue dissociation kit (130-092-628, Miltenyi) and magnetic anti-CD140a microbeads (130-092-628, Miltenyi).

3 .

 3 Set the image analysis by first creating a collection of images (see Note 14) representative of the phenotypes to be monitored throughout the experiment. Select the first picture and click on "Create or Add to Image Collection" (Fig. 1 option A), select the "Basic Analyzer" job type mode and add the image to the new image collection. Select another 4-6 new images (see Note 15) and add them to the same image collection.

8 .

 8 The results will estimate cell proliferation, measured as the percentage of cell confluence (see Notes 18 and 19) or as a phase-contrast object (cell) count (see Notes 11 and 20) during the time-course of the experiment. The results are plotted as the percentage of confluence over time for each well, or as the average of technical/biological replicates (Fig. 2C) (See Note 21).

1 . 2 .

 12 Seed 20,000 astrocytes or 10,000 OPC per well (see Note 10), on a 96-well plate Incucyte ® Image Lock (see Notes 1, 22 and 23) and keep the cells in their medium. Incubate the cells for 1-2 days to obtain a confluent monolayer of astrocytes, or a 70-90% confluent layer of OPC. Place the open plate in the base of the Incucyte® Wound Maker (see Note 24) and carefully introduce the wound maker pin block into the wells. Perform the wound on the bottom of the well by pushing and holding the lever (see Note 25). Wash the cells gently with prewarm astrocyte or OPC medium twice, to remove any cell debris remaining in the wound (while avoiding to disturb the cells) and refill the wells with 100 µL of medium (See Note 7) using a multichannel micropipette.

3 .

 3 After 2 days, replace the medium with oligodendrocyte medium to induce OPC differentiation into mature, fully ramified oligodendrocytes. Change the medium every 2-3 days (see Notes 7 and 13).

2 .

 2 Remove the culture medium. Infect neurons with IncuCyte® NeuroLight Red Lentivirus with the diluted lentivirus at a multiciplity of infection (MOI) of 3 TU/cell (see Note 37) in 200 µL of neuronal medium. Incubate cells with the lentivirus overnight at 37ºC. 3. The next morning (after 8-10 hours of lentiviral infection), remove 190 µL of lentiviruscontaining medium, wash once with neuronal medium, and plate 50,000 previously cultured primary astrocytes in 200 µL of neuronal medium per well, on top of neurons. Perform halfmedium changes every 3 days (see Notes 7 and 13).

Figure 1 .

 1 Figure 1. Incucyte ® Zoom software interface.

Figure 2 .

 2 Figure 2. Live-cell monitoring of OPC proliferation.

Figure 3 .

 3 Figure 3. Astrocyte migration capacity tested by an automated wound healing assay.

Figure 4 .

 4 Figure 4. Individual cell tracking procedure to study the speed, directionality and

Figure 5 .

 5 Figure 5. Semi-automated and label-free measurement of neuritogenesis.

Figure 6 .

 6 Figure 6. Analysis of oligodendrocyte differentiation by semi-automated measurements of

Figure 7 .

 7 Figure 7. Monitoring of neuronal neuritogenesis on a monolayer of astrocytes.

  Figure 1 Braz, Dinca et al.

  1. IncuCyte Zoom videomicroscope equipped with IncuCyte® Scratch Wound Cell Migration

	Software Module (9600-0012, Essen Bioscience) and IncuCyte® NeuroTrack Software
	Module (9600-0010, Essen Bioscience).
	2. Computer equipped with Incucyte Zoom software, FIJI-ImageJ software [27] and DiPer
	program [28].
	3. IncuCyte® Cell Migration Kit (4493, Essen Bioscience).
	4. IncuCyte® ImageLock Plates (4379, Essen Bioscience).
	5. IncuCyte® NeuroLight Red Lentivirus -Synapsin Promoter (4584, Essen Bioscience).

10. The results will measure the changes in the neurite length and number of branching points per well surface of the red fluorescent neurons over time (See Note 35) in the presence of unmarked co-cultured astrocytes (See Note 38) (Fig. 7B).

Acknowledgement

We are grateful to the personnel of CERFE (Centre d'Exploration et de Recherche Fonctionelle Expérimentale, Genopole, Evry, France) and LEAT (Laboratoire d'Experimentation Animale, Imagine Institute, Paris, France) for attentively caring for the mice. We would like to thank the AFM-Téléthon (France), ANR (France) and Paris Descartes -Sorbonne Paris Cité University (France), Imagine Foundation (France), Fondation Bettencourt Shueller (France) and Ministère Français de la Recherche et Technologie (France) for financial support. Our research has received a state subsidy managed by the National Research Agency, under the ''Investments or the Future'' program, bearing the reference ANR-10-IAHU-01 and under the program ANR-10BLAN-1121-01.