Kevin Lebrigand

Joseph Bergenstråhle

Kim Thrane

Annelie Mollbrink

Pascal Barbry

Rainer Waldmann

Joakim Lundeberg

The spatial landscape of gene expression isoforms in tissue sections

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and fulllength sequence heterogeneity cannot be characterized with current methods. Here, we introduce Spatial Isoform Transcriptomics (SiT), an explorative method for characterizing spatial isoform and sequence heterogeneity in tissue sections, and show how it can be used to profile isoform expression and sequence heterogeneity in a tissue context

Main

. Among these approaches, in situ capturing technologies produce whole-transcriptome spatial gene expression by tagging spatial barcodes to transcripts after poly(A)-capture. However, current protocols for in situ capturing are based on short-read sequencing, which entails fragmentation and makes it impossible to detect alternative splicing events, characterize somatic mutations or allele-specific expression.

While spatial profiling of specific isoforms within a tissue context has been demonstrated previously [START_REF] Lundin | Spatiotemporal mapping of RNA editing in the developing mouse brain using in situ sequencing reveals regional and cell-type-specific regulation[END_REF][START_REF] Erben | A Novel Ultrasensitive In Situ Hybridization Approach to Detect Short Sequences and Splice Variants with Cellular Resolution[END_REF], these methods rely on a priori knowledge about transcript architecture. Here, we introduce Spatial Isoform Transcriptomics (SiT), an unbiased method based on spatial in situ capturing to detect and quantify spatial expression of splicing variants (Methods). Briefly, we fix fresh-frozen tissue samples using methanol. After staining and imaging of the samples, we spatially barcode the mRNA molecules. The resulting sequencing libraries are used for both full-length nanopore sequencing and highly accurate 3' cDNA short-read generation. Nanopore sequencing is well suited for spatial transcriptomics because it generates more reads and information than other long read platforms (Supplementary Table 1). Furthermore, it is fully compatible with already available spatial arrays, which have barcoded poly(T)-primers on a glass slide surface. Spatial barcodes and UMIs were errorcorrected and assigned to nanopore reads using the Java toolkit Sicelore [START_REF] Lebrigand | High throughput, error corrected Nanopore single cell transcriptome sequencing[END_REF]. We demonstrate the isoform landscape in situ (Fig. 1a) in two regions of the mouse brain: the olfactory bulb (MOB) and coronal sections of the left hemisphere (CBS) (Supplementary Fig. 12). Molecular markers define distinct anatomical regions as landmarks for analysis (Supplementary Table 2). We identify multiple genes that display alternative isoform expression and sequence heterogeneity in spatially distinct areas of the brain (Supplementary Fig. 3). Our findings were confirmed using in situ sequencing (ISS).

For the MOB sample, we applied a stringent filter to only retain isoforms that contain all exon-exon junctions (mean exon number 6.7) of the reference transcript. 2,386 unique isoform molecules were found, corresponding to 1,076 distinct isoforms per spatially barcoded spot (55 μm diameter) across the tissue section (Supplementary Fig. 4). Short-read clustering resulted in five anatomically defined areas, as previously demonstrated [START_REF] Ståhl | Visualization and analysis of gene expression in tissue sections by spatial transcriptomics[END_REF] (Fig. 1a, Supplementary Fig. 5). Based on this clustering, we identified 19 isoform switching genes out of which Plp1 and Myl6 showed the most prominent regional isoform switching (Methods, Supplementary Table 3). Interestingly, Plp1, a gene involved in severe pathologies associated with CNS dysmyelination [START_REF] Nave | Myelination and support of axonal integrity by glia[END_REF] demonstrated a clear regional difference in isoform expression between the outer regions of the olfactory nerve layer and the inner granule cell layer (Fig. 1b, Supplementary Fig. 6). We validated the identified spatial isoform differences by in situ sequencing (ISS) on a tissue section from another individual (Fig. 1c, Supplementary Fig. 7).

Each spatially barcoded spot typically captures transcripts from multiple cells. However, using single cell RNA-seq data, it is possible to deconvolute the transcriptional signal into the likely constituent cell types of the spot, and to associate specific cell type(s) to isoform expression. We demonstrate this approach using a previously published MOB scRNA-seq dataset [START_REF] Tepe | Single-Cell RNA-Seq of Mouse Olfactory Bulb Reveals Cellular Heterogeneity and Activity-Dependent Molecular Census of Adult-Born Neurons[END_REF] and a deconvolution strategy based on the identification of pairwise cell correspondences [START_REF] Stuart | Comprehensive Integration of Single-Cell Data[END_REF]. This approach identifies the myelinatingoligodendrocyte-based (Mag+) cell type within the Granule Cell layer as the predominant producer of the Plp1 standard isoform and the Olfactory Ensheathing Cell-based (Sox10+) cell type within the Olfactory Nerve Layer as the predominant producer of the truncated Plp1 isoform DM20 (Supplementary Fig. 8).

The same approach of isoform calling was then applied on two independent coronal brain sections. We identified 3,306 unique molecules (UMIs), corresponding to 1,338 distinct isoforms per spatially barcoded spot (Supplementary Fig. 4), with an average of 6.7 exons per isoform. Clustering based on short-read gene expression resulted in 12 anatomically defined regions (Fig. 1d, Supplementary Fig. 5). We found 64 significant isoform switching genes between regions commonly identified in the two replicates (methods, Supplementary Table 3). The reproducibility of SiT and the provided isoform landscape was demonstrated by taking two CBS sections, located 50 μm apart in the tissue (Supplementary Fig. 9). Our data revealed in both sections a pronounced isoform switching of Snap25 [START_REF] Irfan | SNAP-25 isoforms differentially regulate synaptic transmission and long-term synaptic plasticity at central synapses[END_REF] between the hypothalamus and midbrain (Fig. 1e-f, Supplementary Fig. 10), and this pattern was confirmed by ISS (Fig. 1g). Further validations of the regional isoform switching obtained with SiT were performed by ISS for an additional set of genes (Supplementary Fig. 1112).

Our spatial long read data also identified RNA adenosine-to-inosine (A-to-I) editing events. Such editing has been shown to be essential for neurotransmission and other neuronal functions [START_REF] Behm | RNA Editing: A Contributor to Neuronal Dynamics in the Mammalian Brain[END_REF]. While other studies have looked at editing events on bulk samples from mouse brain [START_REF] Licht | A high resolution A-to-I editing map in the mouse identifies editing events controlled by pre-mRNA splicing[END_REF], or spatially resolved by ISS for a limited number of targeted editing sites [START_REF] Lundin | Spatiotemporal mapping of RNA editing in the developing mouse brain using in situ sequencing reveals regional and cell-type-specific regulation[END_REF], none has provided an exhaustive spatially-resolved RNA editing map. To this end, we performed additional sequencing for one of the CBS sections (CBS2) to achieve the necessary level of transcript information for robust calls of single nucleotide variants (SNVs) (Supplementary Table 1). We explored a total of 100,838 A-to-I RNA editing sites described in the literature [START_REF] Licht | A high resolution A-to-I editing map in the mouse identifies editing events controlled by pre-mRNA splicing[END_REF][START_REF] Ramaswami | RADAR: a rigorously annotated database of A-to-I RNA editing[END_REF]. To reach high confidence calls with long-read data, we defined ad hoc thresholds for the number of reads per UMI and consensus base quality by initially looking at the agreement between long and short read base calls for 81,062 shared UMIs (Supplementary Fig. 13, Methods). We kept 57.9% of UMIs that passed this filtering for downstream analysis (Fig. 2a). Globally, we observed an editing ratio of 10.9% for 7,635 editing sites covered by at least one UMI (Supplementary Table 4). Interestingly, editing ratios displayed a non-uniform spatial distribution (Fig. 2b). Thalamus had significantly higher editing ratios while fiber tracts had significantly lower editing ratios (Monte Carlo permutation test p-value ≤ 0.05). Consistent with this finding, we observed a positive correlation between adenosine deaminases acting on RNAs (ADARs) and editing ratios (Fig. 2c, Supplementary Fig. 14).

Among all editing sites, we noticed a site within Calm1 that displayed a particularly robust variation across regions. It showed a particularly high editing ratio in the thalamus compared to the other regions (mean per spot 30.6 % vs. 4.9 %; p-value 9.19e-33) and in the Midbrain (15.1% vs 6.3%; p-value 1.19e-49) (Supplementary Fig. 15). The gene Calm1 encodes calmodulin, which acts as a major intracellular "Ca 2+ -receptor" that controls cellular responses to modifications of cytoplasmic Ca 2+ [START_REF] Hanson | Neuronal Ca 2+ /Calmodulin-Dependent Protein Kinases[END_REF].

The editing site in question is located 2,638 bp from the 3' end within the UTR of Calm1 and has previously been characterized as an allele-specific editing site [START_REF] Zhou | Genome wide analyses uncover allele-specific RNA editing in human and mouse[END_REF].

In conclusion, we provide, to our knowledge, the first genome-wide approach to explore and discover isoform expression and sequence heterogeneity in a tissue context. The SiT methodology is based on easily available reagents and enables a deepened investigation of the isoform landscape, including studies of bi-allelic expression, fusion transcripts, and SNP expression in a spatial context, which we believe will be helpful to understand biological systems and provide an additional layer of information to Cell Atlas initiatives.

Methods

Mouse Brain Samples

Olfactory bulbs were isolated from C57BL/6 mice (>2 months old), snap-frozen in Isopentane (Sigma-Aldrich) and embedded in cold optimal cutting temperature (OCT, Sakura) before sectioning. The left hemisphere was isolated from an C57BL/6J (8-12 weeks old) mouse and processed in the same way. Olfactory bulbs from two different individuals were used for the Visium and ISS experiments, whereas the same sample of the left hemisphere was used for both methods.

10x Genomics Visium experiments

The Visium Spatial Tissue Optimization Slide & Reagent kit (10X Genomics, Pleasanton, CA, USA) was used to optimize permeabilization conditions for mouse brain tissue. Two coronal sections of the left hemisphere (IDs: CBS1 and CBS2) and one section of olfactory bulb (ID: "MOB") were processed according to the manufacturer's protocol. Spatially barcoded full-length cDNA was generated using Visium Spatial Gene Expression Slide & Reagent kit (10X Genomics) following the manufacturer's protocol. Tissue permeabilization was performed for 6 and 9 min (CBS1, CBS2) and 12 min (MOB). cDNA amplification was conducted with 12 (CBS) and 17 (MOB) cycles. A fraction of each cDNA library was used for nanopore sequencing, whereas 10 µl was used in the 10X Genomics Visium library preparation protocol of fragmentation, adapter ligation, and indexing. The libraries were sequenced on a NextSeq500 (Illumina), with 28 bases from read 1 and 91 from read 2, and at a depth of 253, 217, and 210 million reads for MOB, CBS1, and CBS2 samples, respectively. The raw sequencing data was processed with a pre-launch of the Space Ranger pipeline (10X Genomics) and mapped to the mm10 genome assembly.

Oxford Nanopore sequencing

Nanopore sequencing of libraries prepared with cDNA from the 10x Genomics workflow yield 20 -50% reads without the 3' adapter sequence and thus lack the spatial barcode and UMI [START_REF] Lebrigand | High throughput, error corrected Nanopore single cell transcriptome sequencing[END_REF]. To deplete such DNA, we initially selected for cDNA that contains a biotinylated 3' primer. 10 ng of the 10x Genomics Visium PCR product were amplified for 5 cycles with 5'-AAGCAGTGGTATCAACGCAGAGTACAT-3' and 5' Biotine-AAAAACTACACGACGCTCTTCCGATCT 3'. Excess biotinylated primers were removed by 0.55x SPRIselect (Beckman Coulter) purification and the biotinylated cDNA (in 40 µl EB, Qiagen) was bound to 15 µl 1x SSPE washed Dynabeads™ M-270 Streptavidin beads (Thermo) in 10 µl 5x SSPE for 15 min at room temperature on a shaker. Beads were washed twice with 100 µl 1x SSPE and once with 100 µl EB. The beads were suspended in 100 µl 1x PCR mix and amplified for 8 cycles with the primers NNNAAGCAGTGGTATCAACGCAGAGTACAT and NNNCTACACGACGCTCTTCCGATCT to generate enough material (1 -2 µg) for nanopore sequencing library preparation. To deplete small fragments which are typically of little interest for transcript isoform analysis (cDNA from degraded RNA, ribosomal RNAs), small cDNA (< 1 kB) was depleted with a 0.5x SPRI select purification. If fragments between 0.5 and 1 kB need to be retained, SPRIselect concentration should be increased to 0.8x. nanopore sequencing libraries were prepared with the LSK-109 kit from Oxford nanopore (1 µg cDNA) following the instructions from the manufacturer. PromethION flowcells were loaded with 200 ng library each. PCR amplifications for nanopore library preparations were made with Kapa Hifi Hotstart polymerase (Roche Sequencing Solutions): initial denaturation, 3 min at 95°C; cycles: 98°C for 30 sec, 64°C for 30 sec, 72°C for 5 min; final elongation: 72°C for 10 min, primer concentration was 1 µM.

Oxford Nanopore data processing Nanopore reads were processed according to the scNaUmi-seq protocol [START_REF] Lebrigand | High throughput, error corrected Nanopore single cell transcriptome sequencing[END_REF] (https://github.com/ucagenomix/sicelore) with slight modifications. Briefly, to eliminate reads that originate from chimeric cDNA generated during library preparation, we initially scanned reads for internal (> 200 nt from end) Template Switching Oligonucleotide (TSO, AAGCAGTGGTATCAACGCAGAGTACAT) and 3' adapter sequences (CTACACGACGCTCTTCCGATCT) flanked by a poly(T) (poly(T)-adapter). When two adjacent poly(T)-adapters, two TSOs or one TSO in proximity of a poly(T)-adapter were found, the read was split into two separate reads. Next the reads were scanned for poly(A/T) tails and the 3' adapter sequence to define the orientation of the read and strand-specificity. Scanned reads were then aligned to Mus musculus mm10 with minimap2 v2.17 in spliced alignment mode. Spatial BCs and UMIs were then assigned to nanopore reads using the strategy and software previously described for single cell libraries [START_REF] Stuart | Comprehensive Integration of Single-Cell Data[END_REF]. SAM records for each spatial spot and gene were grouped by UMI after removal of low-quality mapping reads (mapqv=0) and potentially chimeric reads (terminal Soft/Hard-clipping of > 150 nt). A consensus sequence per molecule (UMI) was computed depending on the number of available reads for the UMI using the ComputeConsensus sicelore-2.0 method. For molecules supported by more than two reads (RN > 2), a consensus sequence was computed with the SPOA software [START_REF] Vaser | Fast and accurate de novo genome assembly from long uncorrected reads[END_REF] using the sequence between the end of the TSO (SAM Tag: TE) and the base preceding the polyA sequence (SAM Tag: PE). Phred scores for consensus nucleotides were assigned as -10*log 10 (n Reads not conform with consensus nucleotide / n Reads total),(Phred score maximum set to 20). Consensus cDNA sequences were aligned to the Mus musculus mm10 build with minimap2 v2.17 in spliced alignment mode. SAM records matching known genes were analyzed for matching Gencode vM24 transcript isoforms (same exon makeup) as described [START_REF] Lebrigand | High throughput, error corrected Nanopore single cell transcriptome sequencing[END_REF]. To assign a UMI to a Gencode transcript, we required a full match between the UMI and the Gencode transcript exon-exon junction layout authorizing a twobase margin of added or lacking sequences at exon boundaries, to allow for indels at exon junctions and imprecise mapping by minimap2. Detailed statistics of each step of nanopore read processing are provided in Supplementary Table 1.

Count matrices and data analysis

Raw gene expression matrices generated by Space Ranger were processed using R/Bioconductor (version 3.5.2) and the Seurat R package (version 3.1.4). Visualizations of spatial data were generated with the STUtility R package (version 1.0.0). We created Seurat objects for each of the three samples (MOB, CBS1 and CB2) with different assays for the analysis as follows: (i) "Spatial" containing genelevel raw short read data from the Space Ranger output, (ii) "ISOG" containing the gene-level nanopore long read data, (iii) "ISO" containing isoform-level transcript information where only the molecules where all exons are observed are kept, (iv) "JUNC" containing each individual exon-exon junction observation per isoform, and (v) "AtoI" containing editing sites from the RADAR database (mm9 UCSC liftover to mm10) and from the Licht study [START_REF] Licht | A high resolution A-to-I editing map in the mouse identifies editing events controlled by pre-mRNA splicing[END_REF], for which we observed at least one UMI in our dataset. The "AtoI" assay stored non edited UMI count (@counts slot), edited UMI count (@data slot), and the editing ratio (@scale.data slot) per editing site. RDS files with all the assays stored are available on demand.

10x

Genomics Visium data-driven annotation of anatomical regions The Spatial assay was normalized with SCTransform using standard parameters. The first 30 principal components of the assay were used for UMAP representation and clustering (resolution = 0.4). Brain regions defined by clustering were assigned to known anatomical regions based on the Allen Mouse Brain Atlas. Spot clusterings were similar between short and long read data (Supplementary Figure 16). As short read data contains more UMIs per spot, our different representations are based on short read data.

Differential splicing detection

The FindMarkers function in Seurat (logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1) was used to detect genes showing at least two isoforms as markers of different brain regions with a pvalue ≤ 0.05 using the nanopore isoform-level "ISO" assay.

Long-read calibration for spatial gene editing events

To only keep high confidence base calls, the nanopore data were filtered by exploring the percentage of agreement between both sequencing methods as a function of long read number (RN) per molecule and nanopore consensus base quality value (Supplementary Table 5). Long-read molecules having a minimum read number of 3 (MINRN=3) and a base quality value at the editing position of 6 (MINQV=6) were chosen to be of sufficient quality for editing sites calling using the SNPMatrix sicelore-2.0 method.

Editing ratios

Samples CBS2 and MOB were used for calculating global editing ratios. Four capture spots in CBS2 (out of 2,499) and five capture spots in MOB (out of 918) with zero UMIs across all editing sites were excluded from the analysis. To test the significance of our findings, resampling of capture-spots across the sample were performed. Observed editing ratios per spot was kept and each spot were randomly assigned a region-label from the pool of original labels without replacement 10k times. A normal distribution was fitted to the simulated editing ratios to calculate the probability of observing a value equal to, or more extreme, than the observed value. For Calm1, the same approach was used with spots from CBS2.

In situ sequencing validation Two 10 µm cryosections each of the olfactory bulb and coronal sections of the left hemisphere were placed on SuperFrost Plus microscope slides (ThermoFisher Scientific), stored at -80 °C and shipped on dry ice to CARTANA for library preparation, probe hybridization, probe ligation, rolling circle amplification, and fluorescence labeling using the HS Library Preparation Kit (P/N 1110) and for the in situ sequencing using the ISS kit (P/N 3110) and sequential imaging using a 20x objective. The result table of the spatial coordinates of each molecule of all targets together with the reference DAPI image per sample were provided by CARTANA. The list of transcripts that were investigated is listed in Supplementary Table 6.

Figure 1 .Figure 2 .

 12 Figure 1. SiT reveals isoform-switch in the mouse brain. (a) Experimental and computational steps for SiT analysis. Right side shows gene expression clusters in the olfactory bulb. (b-c) Expression of Plp1 isoforms (Plp1-201, Plp1-202, also called DM20) detected by SiT (b) and ISS (c). (d) Gene expression clusters of the left coronal brain section. (e-g) Expression of Snap25 isoforms detected by SiT (e), by SiT and visualized as a ridge plot with regions ordered after mean expression (f), and by ISS (g). The dotted circle indicates the midbrain, and the rectangle the hypothalamus.

Acknowledgements

This project was supported by Institut National contre le Cancer (PLBIO2018-156), FRM (DEQ20180339158), the Inserm Cross-cutting Scientific Program HuDeCA 2018, the National Infrastructure France Génomique (Commissariat aux Grands Investissements, ANR-10-INBS-09-03, ANR-10-INBS-09-02), the Swedish Research Council, Swedish Foundation for Strategic Research, Horizon2020 HCA discovAIR, Knut and Alice Wallenberg Foundation (2018.0172), Erling-Persson Family Foundation (HDCA), and Science for Life Laboratory. We would like to thank the National Genomics Infrastructure (NGI) Sweden for providing infrastructure support. We thank Ludvig Bergenstråhle and Alma Andersson for advice and helpful discussions.

Data availability

All relevant data have been deposited in Gene Expression Omnibus under accession number GSE153859 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153859).

Code availability

All custom software used is available on Github (https://github.com/ucagenomix/sicelore). R figures and analysis scripts are available on Github (https://github.com/ucagenomix/SiT). Nanopore long reads .bam files are available within an interactive IGV web browser for visual inspection (https://www.genomique.info/SiT/). Seurat object .RDS files for the three samples are available on demand.


Pascal Barbry

Amélie Cavard

Ph.D Marc Chanson
email: marc.chanson@unige.ch

Aron B Jaffe

Lindsey W Plasschaert

Regeneration of airway epithelial cells to study rare cell states in Cystic Fibrosis

Keywords: cellular models, RNA-seq, repair, airway epithelium, cystic fibrosis

Pathological remodeling of the airway epithelium is commonly observed in cystic fibrosis (CF).

Thus, tissue repair is critical to restore integrity and maintenance of the epithelial barrier function. Epithelial repair is a multi-step process initiated by progenitor cell migration into the injured area, proliferation, and re-differentiation into all of the cell types that contribute to the function of a normal airway epithelium. Recent technological advances applied to relevant animal and cell injury models have helped in understanding the complexity of progenitor cell differentiation. This short review will introduce the current knowledge of the mechanisms regulating airway epithelial cell (AEC) regeneration and repair, with a focus on the specification of two rare cell types/states: ionocytes and deuterosomal cells.

Introduction:

Different reparative strategies involving basal stem cells play a central role in airway epithelium regeneration. Characterized by a relatively slow turnover, the pseudostratified airway epithelium can be rapidly reconstituted after episodes of infection, inflammation or injury that are commonly observed in respiratory diseases, including CF [START_REF] Randell | Airway epithelial stem cells and the pathophysiology of chronic obstructive pulmonary disease[END_REF][START_REF] Beers | The three R's of lung health and disease: repair, remodelling, and regeneration[END_REF]. The airway epithelium is not only constituted of basal cells (BCs), club/secretory cells (SCs, predominantly found in bronchioles), goblet cells (GCs) and multiciliated cells (MCCs), but also of less frequent cell types including ionocytes, neuroendocrine, tuft, and intermediate progenitor cells like deuterosomal cells (Figure 1). These cell types/states, which all arise from self-renewing and multipotent BCs, contribute to tissue homeostasis and function of the airway epithelium [START_REF] Rock | Basal cells as stem cells of the mouse trachea and human airway epithelium[END_REF][START_REF] Hogan | Repair and regeneration of the respiratory system: complexity, plasticity, and mechanisms of lung stem cell function[END_REF][START_REF] Pardo-Saganta | Injury induces direct lineage segregation of functionally distinct airway basal stem/progenitor cell subpopulations[END_REF][START_REF] Watson | Clonal dynamics reveal two distinct populations of basal cells in slow-turnover airway epithelium[END_REF][START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF][START_REF] Montoro | A revised airway epithelial hierarchy includes CFTR-expressing ionocytes[END_REF](9).

Lineage tracing in mouse models subjected to different airway epithelial injuries has established the dynamics and plasticity of mouse AEC differentiation during tissue repair. It is becoming clear that improper tissue repair may contribute to destructive chronic lung diseases [START_REF] Mcqualter | Endogenous lung stem cells for lung regeneration[END_REF]. In this context, several lines of evidence point to an intrinsic defect in the repair process of the CF airway epithelium. Increased BC proliferation was observed on lung sections from CF patients [START_REF] Voynow | Basal-like cells constitute the proliferating cell population in cystic fibrosis airways[END_REF], on CF airway epithelial regeneration in a humanized nude mouse xenograft model [START_REF] Hajj | Human airway surface epithelial regeneration is delayed and abnormal in cystic fibrosis[END_REF] and in primary cultures of human CF AECs after mechanical injury [START_REF] Crespin | Cx26 regulates proliferation of repairing basal airway epithelial cells[END_REF]. CFTRdeficient airway epithelial cell lines ( 14) and primary cultures of human CF AECs [START_REF] Adam | Cystic fibrosis airway epithelium remodelling: involvement of inflammation[END_REF] undergoing repair after injury showed delay in the kinetics of wound closure, which could be partially rescued with CFTR modulators [START_REF] Trinh | Improvement of defective cystic fibrosis airway epithelial wound repair after CFTR rescue[END_REF][START_REF] Adam | CFTR rescue with VX-809 and VX-770 favors the repair of primary airway epithelial cell cultures from patients with class II mutations in the presence of Pseudomonas aeruginosa exoproducts[END_REF]. It is thought that, in the absence of exogenous inflammation, the regenerated CF epithelium is remodeled, exhibits basal cell hyperplasia, and shows a delay in ciliated cell differentiation.

The combination of relevant animal and cell models (CF pigs, CF human AEC primary cultures) and new powerful methods of single-cell gene expression are likely to shed new lights on the impact of CFTR deficiency on the deregulated mechanisms during regeneration and repair of the CF airway epithelium. The aim of this short review is to briefly describe the different steps of AEC differentiation and emphasize two recently described populations of cells that are relevant for CF, namely pulmonary ionocytes and deuterosomal cells.

Cell lineage in regenerating AECs:

The task of maintaining and repairing the airway epithelium falls mainly on BCs (Figures 1 and2). Basal cells are a heterogeneous population of cells expressing TRP63 (transformation related protein 63) and KRT5 (cytokeratin 5), a subset of which express KRT14. The Yesassociated protein (YAP) is critical for BC maintenance by interacting with TRP63; YAP overexpression has been shown to enhance proliferation, BC self-renewal, and to block terminal AEC differentiation [START_REF] Mahoney | The hippo pathway effector Yap controls patterning and differentiation of airway epithelial progenitors[END_REF][START_REF] Zhao | Yap tunes airway epithelial size and architecture by regulating the identity, maintenance, and selfrenewal of stem cells[END_REF]. YAP interacts with Smad proteins of the BMP (bone morphogenetic protein) and TGF- signaling pathways, which are known to regulate the expression of genes involved in proliferation and development [START_REF] Cibois | BMP signalling controls the construction of vertebrate mucociliary epithelia[END_REF][START_REF] Mou | Dual SMAD signaling inhibition enables long-term expansion of diverse epithelial basal cells[END_REF][START_REF] Tadokoro | BMP signaling and cellular dynamics during regeneration of airway epithelium from basal progenitors[END_REF].

Upon injury, which exposes BCs to the luminal surface, receptors for growth factors are unmasked, triggering their activation and initiating their migration and proliferation [START_REF] Vermeer | Segregation of receptor and ligand regulates activation of epithelial growth factor receptor[END_REF]. This phase of newly proliferating and migrating cells, or blastema (containing suprabasal and early progenitor cells), is paralleled by cell differentiation and lineage decisions (Figures 1 and2). Some BCs appear to function as classic multipotent stem cells, while others are thought to be progenitors already committed to a ciliated or secretory fate [START_REF] Pardo-Saganta | Injury induces direct lineage segregation of functionally distinct airway basal stem/progenitor cell subpopulations[END_REF][START_REF] Watson | Clonal dynamics reveal two distinct populations of basal cells in slow-turnover airway epithelium[END_REF]. Several key signaling pathways regulate the continuous transition between proliferation and differentiation. First, AEC proliferation self-perpetuates until closure of the wound; wound closure triggers signals to stop proliferation via mechanisms that involve Notch and PPAR signaling [START_REF] Chanson | Connexin communication compartments and wound repair in epithelial tissue[END_REF]. The expression of Notch3 in some daughter cells coincides with the generation of early progenitors [START_REF] Pan | Myb permits multilineage airway epithelial cell differentiation[END_REF][START_REF] Mori | Notch3-Jagged signaling controls the pool of undifferentiated airway progenitors[END_REF], while early differentiation is dependent on Notch1 and Notch2 signaling to give rise to SCs [START_REF] Rock | Notch-dependent differentiation of adult airway basal stem cells[END_REF][START_REF] Morimoto | Different assemblies of Notch receptors coordinate the distribution of the major bronchial Clara, ciliated and neuroendocrine cells[END_REF][START_REF] Danahay | Notch2 is required for inflammatory cytokine-driven goblet cell metaplasia in the lung[END_REF]. Although Notch signaling appeared unaltered between CF and non-CF repairing AECs, deficient PPAR signaling was shown in CF cells, which in turn may affect the finetuning mechanisms coordinating the transition between proliferation and differentiation within the blastema [START_REF] Saab | Ineffective correction of PPARγ signaling in cystic fibrosis airway epithelial cells undergoing repair[END_REF].

It is now clearly established that SCs can act as facultative progenitors to give rise to

GCs and MCCs [START_REF] Wansleeben | Stem Cells of the adult lung: their development and role in homeostasis, regeneration and disease[END_REF]. According to Watson and collaborators, SCs are short-lived and give rise to the dominant MCC population in well-differentiated airway epithelia [START_REF] Watson | Clonal dynamics reveal two distinct populations of basal cells in slow-turnover airway epithelium[END_REF]. Late differentiation to MCCs is achieved through cell division arrest and Notch pathway inhibition in response to increased expression of miR-34/449 [START_REF] Marcet | Control of vertebrate multiciliogenesis by miR-449 through direct repression of the Delta/Notch pathway[END_REF]. Of note, attenuation of BMP activity seems also necessary for the specification of MCCs [START_REF] Cibois | BMP signalling controls the construction of vertebrate mucociliary epithelia[END_REF]. The differentiation of other cell types, including mucous cells, ionocytes, and deuterosomal cells, is also influenced by the Notch/BMP/PPAR interplay [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF]9,[START_REF] Revinski | CDC20B is required for deuterosome-mediated centriole production in multiciliated cells[END_REF]. Because ionocytes are highly enriched in CFTR [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF][START_REF] Montoro | A revised airway epithelial hierarchy includes CFTR-expressing ionocytes[END_REF] and deuterosomal cells represent an immature population of MCCs [START_REF] Revinski | CDC20B is required for deuterosome-mediated centriole production in multiciliated cells[END_REF], they may well contribute to the intrinsic remodeling of the CF airway epithelium.

Ionocytes:

The levels and distribution of CFTR expression and activity across the airway epithelium have been investigated by numerous laboratories. However, a detailed understanding remains incomplete at least in part due to a lack of 'gold standard' reagents for detecting CFTR. This large body of work has led to the general view that CFTR is expressed at low levels in ciliated cells [START_REF] Kreda | Characterization of wild-type and deltaF508 cystic fibrosis transmembrane regulator in human respiratory epithelia[END_REF], although early studies using in situ hybridization identified rare, CFTR-high expressing cells in both the airway surface epithelium and the submucosal gland ducts [START_REF] Engelhardt | Submucosal glands are the predominant site of CFTR expression in the human bronchus[END_REF].

Recently, single-cell RNA-seq profiling of primary mouse and human AECs uncovered a rare population of CFTR-high expressing cells, accounting for approximately 1% of the epithelium [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF][START_REF] Montoro | A revised airway epithelial hierarchy includes CFTR-expressing ionocytes[END_REF]. These cells also uniquely expressed the Foxi1 transcription factor, multiple subunits of the vacuolar ATPase (V-ATPase) proton pump, as well as other ion channels. This expression signature is found in ionocytes in teleost fish gills and skin [START_REF] Conte | Origin and differentiation of ionocytes in gill epithelium of teleost fish[END_REF] and Xenopus larval skin [START_REF] Dubaissi | Embryonic frog epidermis: a model for the study of cell-cell interactions in the development of mucociliary disease[END_REF][START_REF] Quigley | Specification of ion transport cells in the Xenopus larval skin[END_REF], as well as in intercalated cells in the mammalian kidney, forkhead-related (FORE) cells in the inner ear, and clear cells in the epididymis [START_REF] Vidarsson | The forkhead transcription factor Foxi1 is a master regulator of vacuolar H-ATPase proton pump subunits in the inner ear, kidney and epididymis[END_REF], and therefore were termed "pulmonary ionocytes".

Despite their paucity, pulmonary ionocytes express approximately 54% of the Cftr transcripts in the mouse trachea [START_REF] Montoro | A revised airway epithelial hierarchy includes CFTR-expressing ionocytes[END_REF]. Furthermore, functional studies measuring channel activity with Ussing chambers found that ionocytes account for 60% of the mean channel current in primary human AEC cultures [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF]. Together, these data indicate that pulmonary ionocytes are a major source for CFTR activity. Lineage tracing in mice indicates that BCs can give rise to pulmonary ionocytes [START_REF] Montoro | A revised airway epithelial hierarchy includes CFTR-expressing ionocytes[END_REF], and immunofluorescence analysis identified Krt5 + Foxi1 + cells in mouse tracheae [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF], suggesting that this differentiation occurs without an intermediate state (Figure 1). Differentiated air-liquid interface cultures derived from human airway BCs also contain pulmonary ionocytes, and FOXI1 overexpression is sufficient to drive the ionocyte transcriptional program in this system [START_REF] Plasschaert | A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte[END_REF].

The identification and characterization of the pulmonary ionocyte raises intriguing new possibilities for CF therapy. Identifying the signaling pathways that drive ionocyte production may lead to strategies for increasing the pool of ionocytes, and ultimately CFTR activity, for patients with partial loss of function CFTR mutations. Alternatively, gene therapy approaches, either gene transfer or gene editing strategies, may need to focus on targeting the ionocytes, or the pool of basal progenitors that will give rise to ionocytes. Finally, previous studies in Xenopus have demonstrated a defect in ciliogenesis in ionocyte-depleted embryos [START_REF] Dubaissi | Embryonic frog epidermis: a model for the study of cell-cell interactions in the development of mucociliary disease[END_REF]. These data suggest a role for ionocytes in airway remodeling and necessitate careful assessment of their abundance in diseased tissues.

Deuterosomal cells:

Multiciliogenesis, the complex process leading to the formation of mature MCCs, involves early E2F-driven transcriptional events, in possible partnership with several geminin-related factors, such as GMNC and MCIDAS, resulting in the expression of MYB, FOXJ1, RFX2, and RFX3, which collectively regulate the expression of a large body of effectors contributing to the formation of multiple motile cilia. A key step of this process corresponds to the massive biogenesis of centrioles in a limited time window, which mostly occurs through an acentriolar structure named the deuterosome.

Single-cell RNA-sequencing has recently been used to reveal lineage hierarchies in the airway epithelium. Data analysis has revealed the expression of many molecules involved in early steps of multiciliogenesis in a specific group of cells, intermediate between secretory and MCCs (Figure 1). This specific population, entitled deuterosomal cells [START_REF] Revinski | CDC20B is required for deuterosome-mediated centriole production in multiciliated cells[END_REF], expresses FOXJ1 but differs from mature MCCs by a unique expression pattern with transcripts that are also expressed in cycling BCs. Specific markers include DEUP1, which expresses a key component of deuterosomes, but also FOXN4, PLK4, CCNO and CEP78. This population is also characterized by a re-expression of cell cycle markers, such as CDK1, CKS1-2, PTTG1, CENPF, CENPU, CENPW, probably in line with their requirement for the biosynthesis of hundreds centrioles.

Deuterosomal cells express specifically CDC20B, the host gene of miR-449abc, which was recently shown as a key regulator of centriole amplification [START_REF] Revinski | CDC20B is required for deuterosome-mediated centriole production in multiciliated cells[END_REF]. CDC20B is required for centriole release and subsequent cilia production in mouse and Xenopus multiciliated cells. It interacts with PLK1, a kinase known to coordinate centriole disengagement with the protease Separase in mitotic cells. Strikingly, over-expression of Separase rescues centriole disengagement and cilia production in CDC20B-deficient MCCs. This work revealed the shaping of deuterosome-mediated centriole production in vertebrate multiciliated cells, by adaptation of canonical and recently evolved cell cycle-related molecules. Detailed analysis of CDC20B revealed the existence of an alternatively spliced isoform, which is more highly expressed and likely corresponding to the major source of miR-449abc [START_REF] Marcet | Control of vertebrate multiciliogenesis by miR-449 through direct repression of the Delta/Notch pathway[END_REF][START_REF] Chevalier | miR-34/449 control apical actin network formation during multiciliogenesis through small GTPase pathways[END_REF][START_REF] Mercey | MicroRNAs as key regulators of GTPase-mediated apical actin reorganization in multiciliated epithelia[END_REF]. Gene set enrichment of the deuterosomal population suggests an association with processes such as cilium assembly, centrosome maturation and cell-cycle mechanisms. Genes involved in mitochondrial membrane constitution are also enriched, suggesting an increased number of mitochondria in these cells.

Inhibition of the Notch pathway is a key step in multiciliogenesis (Figure 2). Single cell analysis shows that this pathway, still active in suprabasal cells, undergoes a clear shift in deuterosomal cells, which is illustrated by a reduction in expression of NOTCH2, NOTCH3, HEY1 and HES4 (9). This major inhibitory signature is enhanced by the expression of CIR1 and SAP30, two known transcriptional co-repressors, and of DYRK1A, an inhibitor of the intracellular domain of Notch. HES6, an inhibitor of Notch pathway, is also enriched in deuterosomal cells. Finally, miR-449abc, which is known to alter the Notch pathway [START_REF] Marcet | Control of vertebrate multiciliogenesis by miR-449 through direct repression of the Delta/Notch pathway[END_REF], is upregulated in deuterosomal cells. Another characteristic of these cells is the activation of the TFG-β pathway, with an increased expression of target genes such as SERPINE1, CTGF, ATF3, TGFBR3 or IRF7 (9). This is consistent with the known role of TGF-β signaling in the regulation of cilia length [START_REF] Tözser | TGF-β signaling regulates the differentiation of motile cilia[END_REF].

Summary and future directions:

Single-cell experiments have highlighted novel cell states, pulmonary ionocytes and precursors of MCCs in human AECs. Many questions remain to be addressed: 1) what is the abundance and distribution of the pulmonary ionocyte throughout the respiratory tract and is it altered in CF? 2) Does the ionocyte have region-specific roles in CFTR-mediated lung homeostasis, as for example, in the ducts of the submucosal glands? 3) Does FOXI1 regulate CFTR expression in other tissues? 4) Are the deuterosomal cells altered during constant renewal of MCCs in the injured CF airway epithelium, in which massive multiplication of centrioles has to take place? 5) What is the contribution of other rare cell types/states (e.g., pulmonary neuroendocrine cells, tuft cells, mucous ciliated cells) to the homeostasis of the normal and CF airway epithelium? Future experiments using advanced profiling technologies, detailed histological analysis, and careful functional studies will be crucial for answering these and other questions, which will help to inform the next wave of CF therapeutic development.

Figure 1 :

 1 Figure 1: Lineage hierarchy of the airway epithelium.

Figure 2 :

 2 Figure 2: Main steps of the airway epithelium regeneration and their regulation.

Acknowledgement: This work was supported by Vaincre la Mucoviscidose (to MC and PB), ABCF2 and the Swiss national Science Foundation (310030-172909) to MC, FRM (DEQ20180339158), the Chan Zuckerberg Initiative (Silicon Valley Foundation, 2017-175159 -5022) to ABJ and France Génomique (ANR-10-INBS-09-03, ANR-10-INBS-09-02) (to PB).


Vandana Desai
email: desai@ipac.caltech.edu

Mark Allen
email: cdsmark.allen@astro.unistra.fr

Christophe Arviset
email: christophe.arviset@esa.int

Bruce Berriman

Ranga-Ram Chary
email: rchary@caltech.edu

David Cook
email: dcook@ipac.caltech.edu

Andreas Faisst
email: afaisst@ipac.caltech.edu

Gregory Dubois-Felsmann

Steve Groom
email: sgroom@ipac.caltech.edu

Leanne Guy
email: leanne.guy@lsst.org

George Helou

David Imel
email: imel@ipac.caltech.edu

Stephanie Juneau
email: juneau@noao.edu

Mark Lacy Nrao

Gerard Lemson
email: glemson1@jhu.edu

Brian Major
email: brian.major@nrc-cnrc.gc.ca

Joe Mazzarella

Thomas Mcglynn
email: thomas.a.mcglynn@nasa.govivelinamomchevastsci

Eric Murphy
email: emurphy@nrao.edu

Knut Olsen
email: kolsen@noao

Josh Peek
email: jegpeek@stsci.edu

Alexandra Pope
email: pope@astro.umass.edu

David Shupe
email: shupe@ipac.caltech.edu

Alan Smale
email: alan.p.smale@nasa.gov

Arfon Smith

Nathaniel Stickley
email: nrstickley@ipac.caltech.edu

Harry Teplitz

Ani Thakar
email: thakar@jhu.edu

Xiuqin Wu
email: xiuqin@ipac.caltech.edu

Chuanfei Dong

Susan Mullally Stsci

Reed Riddle

David Rapetti
email: david.rapetti@colorado.edu

A Science Platform Network to Facilitate Astrophysics in the 2020s

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Key Science Goals & Objectives

The 2020s will see large increases in data volumes from observational facilities and from simulations ( Figure 1 ). In the optical and infrared, LSST, Euclid, and WFIRST will generate hundreds of petabytes of data. In the radio, surveys from the VLA, ASKAP and MeerKAT, and, near the end of the decade, SKA and ngVLA will also total hundreds of petabytes. Simulations supporting the planning and interpretation of these observational projects can meet or exceed these data volumes. These data sets have immense science potential, which will only increase when used together. "Big data" resources are central to the astronomical science goals of the 2020s. This importance is reflected in the large fraction of submitted science white papers that reference these data sets (LSST 158;Euclid 68;WFIRST 144;VLA 71;ASKAP 13;MeerKAT 14;SKA 84;ngVLA 85;total ~600). While the science opportunities described in these white papers are diverse, the inherent (often unacknowledged) data challenges in realizing these opportunities are common "big data" problems. These include fundamental activities such as discovering, querying, visualizing, downloading, storing, reprocessing, analyzing, federating, and sharing large data sets across multiple archives. (Section 2.) Below are a few examples of science white papers that illustrate the opportunities and challenges of astronomical big data in the 2020s and beyond. Paladini et al. (2019) describe how data from Gaia, WFIRST, LSST, Euclid, and CASTOR can be combined to accurately measure the initial mass function of stars and the related mass function of molecular cores across a range of environments and distances. These are fundamental measurements which impact our understanding of stellar evolution, galaxy evolution, and planet formation. Making these measurements will require a multi-wavelength, multi-center analysis of imaging data across large areas of the sky. High-level catalogs produced by the projects are unlikely to meet the science requirements in crowded fields with variable backgrounds, especially for the extended cores. Analysis on such a large volume of pixels will present significant data challenges for many users. Kirkpatrick et al. (2019) describe the science potential of an all-sky infrared version of Gaia to determine the low-mass cutoff of star formation. Such a survey would produce an enormous data set with matching legacy value and data challenges. The associated potential and challenges are increased when this survey is used in combination with other large-area surveys, such as Gaia, 2MASS, WISE, ZTF, PanSTARRS, etc. Already, giant single catalogs like those from Gaia and WISE strain most users' abilities to fully visualize and analyze. Wrobel et al. (2019) argue that observing Intermediate-mass black holes in globular clusters could shed light on the early formation of seed black holes and inform predictions of gravitational wave events. To achieve these science goals, they advocate for a facility like the ngVLA. Nyland et al. (191) also describe how the ngVLA will support studies of AGN feedback. The massive datasets from current and future radio interferometers will produce individual datasets with sizes ~0.1-1 PB. This volume makes it difficult for users to tune their image products to meet their science needs, given that traditional mission computing models cannot produce the entire range of possible processing choices. Analysis of the resulting image products will also be challenging. Already, image cubes from ALMA can reach ~1 TB in size, impossible for most users to view using existing tools and facilities. Wang et al. (2019) describe how systematics in measurements of Dark Energy can be minimized with a new galaxy redshift survey (ATLAS) producing ~200 million galaxy spectra, two orders of magnitude larger than the SDSS Legacy Survey spectroscopic sample. Such a survey would have enormous legacy potential, which would best be realized by supporting the community in meeting the associated big data challenges. Chary et al. (2019) argue that the loose tension between different measurements of some fundamental cosmological parameters could be resolved by processing data from Euclid, LSST, and WFIRST together at the pixel level . Pixel-level projects run into "big data" challenges very quickly, even when full data sets are not being analyzed. Chang et al. (2019) describe how multi-messenger astronomy in the 2020s will require the sharing of data, code, modeling tools, and facility-specific expertise in near-real time to localize the electromagnetic counterparts to gravitational wave (GW) events. Currently, several collaborations and data centers (e.g., the NED-GWF service at IPAC) combine data across the electromagnetic spectrum to facilitate prompt GW localization campaigns. However, in the 2020s, it will be essential to extend their capabilities in a collaborative analysis environment and prepare for the third generation GW and neutrino observatories, which will produce tens of events per hour (Reitze 2019). This will require developing the cyberinfrastructure needed to combine several large-area follow-up surveys (i.e., LSST and ZTF) with real-time alerts (LIGO/Virgo, IceCube, and LISA) and analysis software tools.

The white papers above provide concrete examples of how large data sets will be vital to make progress in specific science areas spanning astrophysics. Moreover, in an additional series of 6 science white papers, Fabbiano et al. ( 2019) emphasize that many paradigm-shifting discoveries in the 2020s will not be made through well-formulated hypotheses based on knowledge of the time, but rather by an exploratory discovery approach enabled by new telescopes and instrumentation, as well as by high-quality data products in easily accessible and interoperable science archives.

Big Data Challenges in the 2020s

One of the ways that "big data" enable science is by providing statistically significant samples. Large samples underpin future advances in nearly all fields of astrophysics, including studies of exoplanet systems, stellar populations in the Milky Way, galaxy evolution, dark matter, and dark energy. In the current archive model, scientists can search for data satisfying a number of constraints and visualize the results using a web application, but must ultimately download the data for analysis. As samples grow, each of these steps becomes more challenging. The obstacles are even larger when science goals require a combination of large data sets housed at different data centers. Indeed, a survey conducted by the NASA/IPAC Infrared Science Archive in 2018 reveals that astronomers are already struggling with "big data" problems (see Table 1).

The following is a list of major challenges that will need to be met in order to realize the full science potential of astrophysics in the 2020s.

• Searching through large data sets: To identify populations within very large data sets, users need to set a number of search constraints. Traditionally, the most common constraints entered by users have been spatial coordinates or object names. In the era of large and all-sky photometric and spectroscopic surveys, these constraints are being swapped out or augmented by more complex constraints on color, flux, redshift, time, and other observables. During the 2020s, maintaining reasonable response times even for traditional queries --as the volumes of archives continue to explode --will be a major challenge. ) are becoming more important as they provide tools to visualize correlation in 2 highly multi-dimensional data sets at a small cost of CPU/GPU time. Practical examples include the inspection of billions of images (e.g., of galaxies) or classification of light curves using machine learning, which would be an impractical task for a visual inspection by humans.

• Downloading and storing large volumes of data : Currently, once a data set is identified, users typically download it to an individual computer or a departmental cluster to proceed with scientific analysis. However, as data sets continue to grow at an unprecedented rate, and scientists tackle problems that require larger and larger data sets to solve, this download step is becoming more challenging and in some cases completely impractical. First, the time it takes to download data through the network will become prohibitively long, even with improvements to the networking infrastructure. Second, downloading even a small percentage of a petabyte-scale data set requires a large amount of storage, both for the downloaded data and for intermediate and user-generated data products that may also be at petabyte scales. While raw storage is becoming increasingly affordable, it is not an out-of-the box solution for most astronomers because of the difficulty of making storage perform well enough to support efficient high-performance computation. For these reasons, the bulk of the premier big data sets of the 2020s will be stuck at the data centers. • Analysis: The availability of "big data" in the 2020s will cause a change in the nature of astronomical analyses. ○ Astroinformatics & Astrostatistics: Siemiginowska et al. ( 2019) describe how advances in statistics, computer science, and machine learning are becoming standard in the workflow of astronomical research. The growing complexity of analyses coupled with the growing sizes of data sets will result in a growing need for computational power, at a level that will be out of reach for many astronomers. ○ Multi-archive interoperability: Joint analyses of data sets that are hosted at different locations can become challenging when large numbers of pixels need to be processed together, when huge catalogs need to be cross-matched, and when constraints must be applied across multiple large catalogs. Since these represent a large number of the desired use cases, data centers need to be interoperable in finite, carefully selected, and well-defined ways. In the future, some data sets may be distributed across data centers (e.g., the SKA regional centre model), which stresses the need for interoperability. ○ Reproducibility & Replicability: It is essential to science that results can be verified by independent groups. Verification in the 2020s will be a challenge because few groups will have the resources and data skills to reproduce (run the same software on the same data to get the same result) or replicate (write software based on analysis descriptions to get similar results) complex analyses on huge data sets spread over multiple data centers. • Accessing adequate computing facilities : The computational aspects of big data analytics can be challenging to astronomers in several ways. First, basic computational techniques that have been in use throughout the 2010s will be too inefficient to deal with the larger data sets of the 2020s. Analysts of the 2020s will need to learn to optimize code for speed and parallelism. Second, astronomers will need access to the considerable computing resources that are required to support petabyte-scale analyses. Compute resources at adequate scale will be located at sites remote from the data host. For this reason, the use of services such as those at the Texas Advanced Computing Center (TACC) and those offered by commercial cloud providers such as Amazon Web Services (AWS) will accelerate in the 2020s.

In particular, fast provisioning and unlimited power to scale will make clouds especially appealing. Astronomers are already taking advantage of this (e..g Toomey et al 2017). Paying for cloud resources and moving petabyte-scale data from the data centers to a cloud resource will remain challenging. • Collaboration: Research has long been a highly collaborative effort. This is now being reflected in the rise of collaborative software tools. From calendars to messaging systems to document writing, scientists expect their tools to support collaboration. New technologies like Jupyter notebooks are currently opening up powerful new opportunities for collaboration during data analysis. The importance of collaboration will increase as data analysis becomes more complex to support the science of the next decade. The challenge will be to support the need for collaboration within the "big data" computing environment of the 2020s. Currently, most data centers provide web APIs that can be used to search for and download data from the data center to a user's local computer. APIs will continue to be necessary in the science platform context, and should follow (when practical) the standards established by the International Virtual Observatory Alliance (IVOA). 3. Web Portals. Web portals are the traditional access points for astronomical data.

Most started with simple search and download capabilities, but have evolved to accommodate more sophisticated search criteria, to allow previews of data products, to provide visualizations of catalogs and survey footprints, and to offer other data exploration capabilities. In the 2020s, these web portals will be providing more sophisticated analysis tools designed to run at the data center, in addition to the above described interactive and batch analysis environments.

The development of science platforms will absorb many of the challenges described in Section 2. This transition is already underway; existing science platforms are listed in Table 2. However, joint analyses of data sets that are held across data centers will be far easier if these individual science platforms are interoperable . We therefore advocate for a science platform network , by which we mean that (1) astronomers will have access to a network of loosely coordinated science platforms that together provide access to a wide array of astronomical data; (2) these science platforms will share common elements (e.g. data models and APIs) so that astronomers do not encounter vastly different user experiences when accessing different data sets; and (3) moderate volumes of data can be transferred between science platforms to facilitate multi-wavelength science.

While we anticipate that a large fraction of science analysis will be performed within and across platforms, the most effective implementation of a platform network would support scalable access to standardized tools and APIs that would allow researchers to use the same approaches --and often the same scripts and codes --on all scales from personal laptops to global analysis. Scientists can test analyses on locally available subsets and then easily transition analyzing complete holdings on platforms. By ensuring that the analysis capabilities in and across platforms mirror those at smaller scales --with standardized software and interfaces adapting to the changing environment --we can greatly ease the adoption of platform resources across the community.

To optimize the science benefits of a science platform network for astronomy, data centers will need to make progress in the following areas:

Collaborative Workspaces. To enable analysis environments co-located with the data, science teams will require workspaces that consist of secure, permanent storage volumes for results that need to persist, and much larger temporary storage volumes for scratch space. These workspaces will be most effective for science if they support collaboration. The IVOA VOSpace concept and WebDAV are two options for supporting collaboration that have been adopted by existing or developing science platforms. The challenge for data centers will be to manage the safety, security, and fair distribution of workspace resources in the face of increasing and variable demand. In the future, these workspaces will most likely be inside commercial cloud services.

Cloud Computing . Individual scientific investigations often require large amounts of computing for limited times. In addition, data centers currently see spikes in the number of queries and download volumes in the period immediately following a large data release. In the science platform context, these spikes will be accompanied by spikes in the demand for computational resources. Because it is not cost effective for data centers to provision for peak demand, they will need to consider augmenting in-house computing resources with dynamically-scalable commercial cloud computing resources, perhaps using a cloud-bursting model. While we anticipate that the cost of commercial cloud services will decrease over the years, these costs will need to be negotiated to fit within the financial model of the data centers. Regardless, there will be research teams that require computational resources that are beyond what can be provided through data centers. Science platforms will therefore be most effective if they can be containerized and deployed into the commercial cloud by individuals as needed.

Containers . Container technologies (such as Docker) encapsulate dependencies between components of a software system, allowing users to run an application reliably in multiple computing environments. Containers reduce the cost to deploy, operate, and maintain instances of science platforms. They also enable the exchange of user-written software between science platforms. In wide commercial use, they are an essential technology for the development of science platforms.

Networking and Data Transfer Tools . Although science platforms will absorb much of the burden of moving petabyte scale data, it will still be convenient for users to be able to move moderate amounts of data to their desktops or between science platforms. Therefore, data centers should ensure that they have state-of-the-art networking infrastructure and data transfer tools, which we anticipate will provide significant performance increases over the decade.

groups, e.g. mirror Millennium simulation database and raw data; Kepler light curves; Non-astronomical data sets. Platform facilities should be specifically funded to work together to ensure that platforms are built to maximize collaborative capabilities. Since the required underpinning technologies are fast evolving and dependent on broader industry standards, we propose implementers design functional and interoperable systems, and then iterate within the IVOA structure to find workable long-term standards, as has worked with protocols like CAOM. While it is premature to propose a specific collaboration structure, the NASA Astronomical Virtual Observatories group (NAVO; https://heasarc.gsfc.nasa.gov/vo/summary ) may be a useful model to build on.

STScI Science Platform (in

Funding agencies should negotiate rates or credits with cloud infrastructure and computing service providers for U.S. researchers and for U.S. data centers building and supporting cloud-based archive and science platform services.

Schedule & Cost

Because of the diversity of data centers, in terms of current and future data holdings, adopted technologies, and funding streams, the cost and schedule of collaboratively implementing a science platform network is difficult to summarize without significant study. We suggest that this study be undertaken by the individual data centers as soon as possible. However, we can safely say that the schedule for development of the science platform network would ideally be driven by the schedule for the "big data" missions and projects of the 2020s, as described in Section 1. We suggest that a phased development is important to slowly acclimate the community to the change in community data infrastructure that will be needed. In the first phase, through FY23, development could focus on implementing individual science platforms, essentially continuing the trend seen in Table 2, while keeping in mind that the second phase, FY23 and beyond, will involve the implementation of the "network" capabilities described above.

Figure 1 .

 1 Figure 1. The 2020s and beyond will see large increases in data volumes. Approximate expected data volumes in terabytes of selected astronomical observational facilities and surveys are shown as a function of time. Symbols are plotted at the (expected) end of operations. Ongoing surveys as of this writing are plotted in 2019 with an arrow. The current size of major data centers are shown on the right axis.

  Development) https://mast-labs.stsci.io/2019/02/zero-to-jupyterhub-with-ansible HST, TESS 4. Recommendations Key Data Centers in the U.S. ground-and space-based astronomy community should be funded to deploy, maintain and extend science platforms by the end of the 2020s. The funding includes (a) development of software components, (b) investment in computing and storage resources for user computing, (c) operating expenses for the resulting systems and (d) resources for coordination.

Table 1

 1 Science Platforms give the user control over all aspects of scientific work, as needed, including data discovery, data access and data exploration, data and model integration, deploying new analysis tools and integrating them with existing tools, running production analysis and modeling, and collaboration and sharing results. This evolution of community data services, in combination with workforce training(Norman et al. 2019, Besla et al. 2019) are necessary to meet the science goals enumerated in the submitted science white papers, as well as science goals that have not yet been imagined. Science platforms increase the accessibility of both data and analysis resources, supporting new types of scientific analysis demanded by petabyte-scale data sets, as well as the inclusion of astronomers at all institutions(Peek et al. 2019). A science platform has three essential aspects: an analysis environment close to the data, scripted data access, and web portals. It is also essential that users can share results between these aspects. This aspect represents the largest change to the current data center model. Science platforms offer large-scale storage and computing resources at the data center itself, eliminating the need to download large volumes of data and to procure storage and computing resources for petabyte-scale analyses. Computing resources are provided as a secure, interactive, and collaborative environment, allowing users to develop and share analysis tools and products. Analysis tools could include the command line, common community-created analysis packages (e.g. CASA, Tractor, ds9), Jupyter notebooks, and whatever tools the user chooses to upload. Once analysis is complete, users can download the summary results. New software tools developed by the community can be integrated into science platforms, but this does require training to ensure that new tools are designed and developed with modern software engineering processes (See the white paper "Elevating the Role of Software as a Product of the Research Enterprise" by Smith et al. 2019). We anticipate that providing these resources will likely rely heavily on adopting cloud technologies (see below). 2. Application Program Interfaces (APIs). APIs allow scripted access to data.

	: Survey Results: "Which of the following problems have you encountered when
	dealing with astronomical data sets?"
	"Big Data" Challenge	Respondents
	I struggle to find the data sets I need.	39%
	The data set I want takes too long to download.	35%
	I struggle to obtain enough storage space to manage the data I want.	30%
	I struggle to obtain enough computing power to analyze my data.	22%
	I struggle to scale my analysis routines to large volumes of data.	27%
	I struggle to visualize data.	30%
	3. Vision for the Future: Science Platform Network for Astronomy

We advocate for the development of a "science platform network" to address the data challenges of reaching the science goals of the 2020s and beyond.

1. Interactive and batch analysis environment co-located with data.

Self organizing maps (SOM), https://en.wikipedia.org/wiki/Self-organizing_map

t-Distributed Stochastic Neighbor Embedding (t-SNE), https://lvdmaaten.github.io/tsne/

http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/search/

Standardized data models and web APIs . An astronomical data model is a standard way of describing a data set. This standardization allows data centers to easily provide search and retrieval options for data sets physically held at other data centers. One example is the Common Archive Observation Model (CAOM) that was developed by the Canadian Astronomy Data Centre ( CADC) and is being taken up by a growing number of data centers. An astronomical web API provides access to a data center's holdings over the web using HTTP protocol. Many astronomical data centers have implemented web APIs that follow the IVOA's Table Access Protocol (TAP) for searching catalogs and tables of image metadata. CAOM, along with standard APIs, enables CADC's Multi-Archive Query option , which offers users access to data hosted at multiple data 3 centers, in addition to the data held at CADC.