Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the transcriptome of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Gene Expression, Isolation, Labeling

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a. The proportions of UMIs from mitochondrial genes per cell (n = number of cells in each library, indicated on the right) and the total number of cells passing filters in each of the 15 libraries comprising the adult dataset. Names indicated correspond to the names in the Seurat object provided (Adult.rds, GSE142787). Boxplots display the first, second and third quartiles. Whiskers extend from the box to the highest or lowest values in the 1.5 inter-quartile range, and outlying datapoints are represented by a dot. b, Origin of the cells in the final adult clusters, colored as in (a). Green arrows: clusters whose unique library distribution can be explained by variable contamination from surrounding tissues (cluster 3 is photoreceptors, 112 is likely Kenyon Cells from the central brain) or the number of lamina neuropils dissociated (clusters 107, 108, 109 are lamina neurons). Red arrows: clusters likely enriched in low quality transcriptomes, as they are enriched in cells from libraries with high number of mitochondrial genes (38, 120, 192) or high number of cells sequenced (102, likely corresponding to multiplets). Brackets: Glial clusters, some of them enriched in libraries with high number of mitochondrial genes as ambient RNA is more similar to RNA from glial vs . neuronal cells . c, Number of clusters obtained with different pairs of clustering parameters. Red rectangle: pair of parameters used. d, Left: Legend as in . Right: Number of isolated neuronal type transcriptomes matching to 1–5 of our adult clusters, for each pair of parameters in (c), which we used as a measure of the biological relevance of our clusters. Matching was defined by the presence of a correlation gap above 0.05 (Methods). We took into account any correlation gap between the 6 best correlated clusters, since similar cell types or overclustering can affect the size of the first correlation gap as illustrated on the left graphs. Red rectangle: pair of parameters used. e, tSNE visualization of the adult optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cell colors indicate the cluster they belonged to before we merged artificially split clusters (red circles, Methods). f, Heatmap showing scaled log-normalized non-integrated expression of the top20 cluster markers between the merged clusters. Merged clusters had almost indistinguishable gene expression patterns, but often differed by their proportions of UMI from mitochondrial genes per cell or the expression levels of the genes highlighted in red, which are enriched in the “ambient RNA cluster” 192 (see also ).

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Isolation, Gene Expression, Expressing

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated Repo+ (glial marker) or Elav+ (neuronal marker) populations. LQ = clusters containing a proportion of cells with features of lower quality transcriptomes. b, Violin plots of features tending to be higher (proportions of UMI from mitochondrial genes) or lower (number of UMIs or genes per cell) in low quality cells , . c, Heatmap showing the scaled log-normalized non-integrated expression of the top5 cluster markers of the adult dataset. The first 5 neuronal adult clusters (1 to 6, cluster 1 and 2 having been merged) are plotted for reference as they clearly have specific gene expression patterns. Clusters 38, 85, 102 and 120 present much less defined gene expression patterns and likely contain low quality neuronal transcriptomes (see also ). Clusters 188 and 189 could be further separated in two groups with different gene expression patterns, as illustrated by the dashed line in the insert. Cluster 191 expresses several markers found in no other clusters and likely correspond to neither glia nor optic-lobe neuron. Cluster 192 expresses mainly low levels of glia-specific genes, without specific markers. It likely corresponds to ambient RNA, which would be enriched in RNA from burst glial cells.

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Expressing, Isolation, Marker, Gene Expression

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated neurons , . We represented Dm3, Tm9, T4 and T5 before their split into Dm3a/b, Tm9v/d, T4/T5ab and T4/T5cd. When two transcriptomes were published for a given neuronal type, the one presenting the highest correlation gap is displayed in this figure. R1–8: average gene expression of all photoreceptors . KC: Kenyon Cells, cluster 112 therefore likely corresponds to contamination from the central brain. b, Legend as in (a). We indicated several matching clusters to highlight the high similarity between LC cells transcriptomes, which explains the lower correlation gaps observed for these neurons. c, Left: Legend as in (a). Right: mixture modelling of Pm3 markers (y axis). Clusters are spread on the x-axis, with the probability of expression of the markers figured by the size of the black dots.

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Expressing, Isolation, Gene Expression

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells colors indicate the clusters they belonged to according to unsupervised clustering (a), or the adult clusters they were classified as by the neural network (b, same as in ). Black circles indicate high granularity regions, where less frequent cell types were grouped together by unsupervised clustering but could be resolved accurately by the neural network (b). c, Same as in (a-b) but cells are named and colored by the adult cluster they were classified as by Seurat label transfer (Methods). d, tSNE visualizations (same as c) including only the cells that were assigned inconsistent identities by Seurat and the neural network. Highest rates of inconsistencies were observed in the center (LQ cells), in L1 and L2 clusters (red ellipses), in most glia clusters (green ellipses), the TE neurons and a glia-like cluster (identity 214, ) with no adult correspondence (blue ellipses). e-f, tSNE visualizations of 56,902 cells sequenced from whole fly brains , using 120 principal components calculated on the log-normalized gene expression. e, Cells are named and colored by the clusters they were classified as by our neural network. f, Cells are named by the cluster identities from the original study and colored by the confidence score they received from our neural network. Black circles mark the following central brain clusters (from left to right): Poxn, OPN, clock neurons and dopaminergic neurons, that all received low scores from the neural network. Kenyon cells (red circles) were assigned with high confidence as our adult dataset was contaminated by them (cluster 112).

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Gene Expression

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: tSNE visualizations of all optic lobe single-cell transcriptomes acquired for this study, using 120 principal components calculated on the log-normalized integrated gene expression. The cells are named and colored consistently at all stages by the neural network classifications with manual adjustments as detailed in . Blue ellipses: Dm3 and Tm9 neuronal subtypes, which could only be resolved at P50 and earlier.

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Gene Expression

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network (NN) or by (b) the log-normalized non-integrated expression of Fs (green), dimm (blue), and skl (red), which are co-expressed in TE neurons (red ellipses). c , Violin plot of log-normalized non-integrated prt expression in all clusters at P50. TE neuron clusters are indicated by circle. d , R10D10-Gal4 co-expression with anti-Prt staining in a P50 optic lobe (n=15 neurons). Scale bar: 10 μm. e , FLEXAMP memory cassette labeling of R10D10-Gal4 in an adult optic lobe (n=28 brains) with anti-NCad staining. Scale bar: 30 μm. f, R10D10-Gal4 expression pattern in L3 optic lobe (n=15 brains), with anti-NCad, anti-Bsh and anti-Hth staining. Arrow: Bsh + , Hth - neurons labeled by R10D10-Gal4 . Scale bar: 30 μm. g-h, R10D10-Gal4 sparse expression at P30 (n=40 neurons), with anti-NCad, anti-Bsh and anti-Hth staining. Scale bars = 5 μm (g) and 15 μm (h). d/pMe: distal/proximal Medulla, Lo: Lobula, Lp: Lobula plate. I , Co-labeling of R10D10-LexA expression and bsh-Gal4 FLEXAMP memory cassette with anti-nCad staining in a P50 optic lobe (n=13 brains). Dashed ellipses: TE neurons. Scale bar: 20 μm.

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Gene Expression, Expressing, Staining, Labeling

Journal: Nature

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

doi: 10.1038/s41586-020-2879-3

Figure Lengend Snippet: a-b, tSNE visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network or by (b) the log-normalized non-integrated expression of dpn (green), ase (blue), and grim (red). Circles match to those of . c, UMAP visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are colored by the log-normalized non-integrated expression of nerfin-1 (green), Hey (blue), and vfl (red) d, UMAP visualization of Tm3 and T1 cells (above and below the dashed line, respectively) from all stages sequenced in this study, using 25 principal components calculated on the log-normalized non-integrated gene expression. Cells are colored by their developmental stage. e, Ventral and dorsal Transient Extrinsic (TE) neurons as well as transient photoreceptors (PRs) line the edges of all optic lobe neuropils and express Follistatin ( Fs ). Moreover, TE and at least 3 other neuronal types express Wnt4 in the ventral medulla/lobula but express Wnt10 in the dorsal part of these neuropils. f, The transcriptome of neurons from the same neuronal type but produced days apart converge towards a similar transcriptomic state, which they reach by P30. Moreover, the inter-neuronal type transcriptomic diversity is highest during P40-P70.

Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

Techniques: Gene Expression, Expressing, Produced

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Information regarding the possible resolution for various de novo genome sequencing technologies

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques: Sequencing

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Ten different assembly strategies using a variety of different data types: PCR-free Illumina short-read (“PCR-free”), long mate pair (“LMP”), 10x Genomics Chromium library (“10x”), and Bionano Genomics optical maps (“Bionano”). The blue-boxed assemblies all originate from the same PCR-free w2rap assembly (A1), and the black-boxed assemblies all originate from the same 10x Genomics Supernova assembly (A3). Information in paretheses refers to assembly software pipeline, and assembly numbers are annotated below each assembly.

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques: Software

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Genome assembly statistics (for sequences >1 kb) for all assemblies

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques:

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Comparison of the number of breaks introduced by REAPR for each of the technologies used to scaffold the w2rap-only assembly (A1)

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques: Comparison

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Comparison of the number of breaks introduced by REAPR for each of the technologies used to scaffold the 10x assembly (A3)

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques: Comparison

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Repeat content of assemblies

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques:

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: Cumulative z -scores of assemblies (solid black circles). Error bars represent the minimum and maximum cumulative z -score after removing each metric in turn and recalculating the z -score for each assembly. Wide error bars show assemblies that are strongly affected by a given metric. For example, the 10x + lmp + bionano assembly (A8) has a long lower-boundary error bar because it has an exceptionally high scaffold N50 z -score (double that of the next nearest ranking assembly) and hence omitting this metric results in the assembly scoring much lower.

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques:

Journal: GigaScience

Article Title: Sequencing smart: De novo sequencing and assembly approaches for a non-model mammal

doi: 10.1093/gigascience/giaa045

Figure Lengend Snippet: REAPR statistics showing the percentage of error-free bases in the assembly, N50s before and after breaking at breakpoints, the percentage decrease in scaffold N50 after breaking, and the fragment coverage distribution (FCD) errors including errors across gaps

Article Snippet: The 10x Genomics Chromium library was assembled using the 10x Genomics Supernova software [ ], using default parameters.

Techniques:

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Data sources for advancing computational modeling “Not So Big” and “Big” data sources can be utilized collectively to build computational models of varying complexity. “Not So Big” data are usually disparate, require extensive data collation, and must be obtained individually from various literature sources in the form of data tables present in the supplementary material. The “Not So Big” data are derived from targeted and focused experiments and provides tissue-level detail for mechanistic models such as the first cell cycle gene regulatory network from Tyson (1991). Bulk and single-cell “Big” data are derived from targeted and unbiased assays, and are usually stored in annotated collections and compendiums such as GEO (transcriptomics), ArrayExpress (transcriptomics), MetaboLights (metabolomics), and PRIDE (proteomics). This “Big” data provides genome-scale detail for informing correlation networks and genome scale metabolic models. Single-cell “Big” data from reference atlases, including The Cancer Genome Atlas, the Human Cell Atlas, HuBMAP, and Tabula Sapiens , provide untargeted and unbiased assays at the whole-body physiological scale. These data can be utilized to inform future virtual human models at various scales, including the molecular level (i.e., Wnt/B-Catenin signaling pathway), single-cell level (i.e., gene correlation networks), and physiological level (i.e., multi-organ interactions). Figure 1 adapted from Tyson (1991), Gustafsson et al. (2023), Park et al. (2016), Moss et al. (2021). Created using biorender.com .

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Derivative Assay

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Molecularly targeted methods for single-cell and spatial transcriptomics

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques:

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Examples of transcriptome-proteome multiomics technologies

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Single-cell Isolation, RNA Detection, Reverse Transcription, Mass Cytometry, Staining

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Computational models informed by experimental data The components, interactions, correlations, and patterns extracted from “Big” data (multi-omics data including transcriptomics, proteomics, metabolomics, and spatial omics) and the components, interactions and mechanisms extracted from “Not So Big” data (i.e., western blots, immuno-staining, and qPCR) can be utilized to generate and inform molecular signaling networks, putative cellular networks, and gene regulatory networks. For instance, while the MAP kinase pathway was discovered using “Not So Big” data sources (solid line) many “Big” data sources (dashed line) have confirmed and further explained and complemented these initial findings. Similarly, while gene regulatory networks have been mainly developed using “Big” data (solid line), “Not So Big” data (dashed line) can also be informative when generating such networks. For example, Park et al., (2016) modeled neurons during the circadian cycle. First, five neuronal groups were identified according to their unique transcriptional landscapes with marker genes shown for each of the groups. A gene regulatory network was then developed based on the major molecular interactions between key neuropeptides (VIP, AVP, PROK2, and PACAP) and the neuronal groups. “Big” and “Not So Big” data (solid lines) have be analyzed in combination to identify putative cellular networks. Cell types can be identified within the “Big” data by using information from “Not So Big” data. Then, cell states within each cell type community can be determined by molecular markers. The cell types and states can then be used to infer cell state transitions, trajectories, and interactions. A greater influence of “Big” and “Not So Big” data on developing the various networks is shown with solid lines with lesser influence shown by dashed lines. GF: growth factor, GFR: growth factor receptor, VIP: Vasoactive Intestinal Peptide, AVP: Arginine Vasopressin, PROK2: Prokineticin 2, PACAP: Pituitary Adenylate Cyclase-Activating Polypeptide. Fig. adapted from Park et al., (2016). Created using biorender.com .

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Biomarker Discovery, Western Blot, Immunostaining, Marker

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Computational models informed by single-cell omics (A) Single-cell omics, including transcriptomics, proteomics, and metabolomics can be used for modeling tumor cell differentiation dynamics. The specific cell types of interest that were identified within the tumor tissue include stem, progenitor, and differentiated cell types. State transitions, trajectories, and interactions between these cell types can then be inferred such that a network model can be generated. The tumor cell differentiation model can then be simulated to determine how the individual cell populations within the tumor change over time. (B) Single-cell omics experiments can be performed on the liver following resection to elucidate liver-specific cell types including Kupffer cells, Stellate cells and hepatocytes. For simplicity, we only show the hepatocyte cell states (replicating, quiescent, and primed), which are informed by molecular markers from the single-cell data. State transitions, trajectories and interactions can then be inferred from the cell states. A systems network model of liver regeneration can then be developed using the features extracted from the single-cell data and the model can be simulated for liver mass recovery and cellular dynamics. The total mass recovery as well as the populations of primed and replicating hepatocytes populations during regeneration are shown. Additionally, the populations of pro- and anti-regenerative stellate cell populations during regeneration are shown. Figure 4 A adapted from Nazari et al., (2018). Figure 4 B adapted from Cook et al., (2018). Created using biorender.com .

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Cell Differentiation, Generated

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Highlighted algorithms for computational modeling informed by single-cell and spatial omics data

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Expressing, Gene Expression, Spatial Proteomics

Journal: iScience

Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

doi: 10.1016/j.isci.2024.111322

Figure Lengend Snippet: Patient-specific models informed by omics data (A) Metabolomics, transcriptomics, and proteomics data can be collected from a patient’s liver sample. A patient-specific genome scale metabolic model (GEM) of the liver can then be generated by integrating the transcriptomics and proteomics data with a generic GEM (i.e., Human1 or Recon2 ). Metabolic fluxes are constrained using the metabolomics data and predicted by flux balance analysis. (B) Bulk and single-cell RNA-seq data can be utilized to generate context-specific metabolic models in health and disease (i.e., liver disease). Metabolic fluxes can be predicted by flux balance analysis and significantly perturbed metabolic pathways/subsystems can be identified in health vs. disease. For example, our analysis of liver transcriptomics data from alcoholic liver disease identified significant metabolic dysregulation in the glutathione (GSH) metabolic pathway. Specifically, the metabolic flux activity of specific solute transporters (LAT1, BAT1, OATP1A2) within the GSH pathway decreased with liver disease, while healthy livers showed an increase in flux along the pathway. (C) Zone-specific hepatocyte populations can be elucidated from single-cell omics data sources. The metabolic expression for genes in the B-oxidation and gluconeogenesis pathways decreases from zone 3 to zone 1, while it increases from zone 3 to zone 1 for genes in the glycolysis and lipogenesis pathways. Marker expression for each of the zonated hepatocyte populations within the “Big” data can be utilized in conjunction with “Not So Big” experimental data (i.e., neural tracings, calcium imaging, and glycogenolytic distribution analyses) to parameterize and structure a computational model of liver innervation, calcium signaling, and glycogenolysis. Additionally, the extent of innervation to the liver can be tuned in the model to the species of interest based on physiological evidence from the literature. Figure 7 B adapted from Manchel et al., (2022). Figure 7 C adapted from Verma, Manchel et al., (2021). Created using biorender.com .

Article Snippet: For example, the output for the 10X Genomics single-cell transcriptomics method is transcript reads, which is stored in a raw FASTQ file.

Techniques: Generated, RNA Sequencing, Activity Assay, Expressing, Marker, Imaging