Journal: Cell reports methods

Article Title: Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools.

doi: 10.1016/j.crmeth.2024.100763

Figure Lengend Snippet: Figure 1. The BARtab and bartools workflow for high-throughput cellular barcoding analysis BARtab is a Nextflow pipeline to process high-throughput sequencing datasets containing cellular barcode information. BARtab contains two main subworkflows that permit the analysis of (1) population-level cellular barcoding data (‘‘bulk workflow’’) and (2) single-cell datasets (‘‘single-cell workflow’’). For the bulk workflow, BARtab takes single or paired-end datasets in fastq format as input and performs read merging (paired-end only), quality filtering, and adapter trimming. Filtering and error correction of barcodes can occur using a reference-based (recommended) strategy, which aligns putative barcode sequences to a user-supplied reference of known lineage barcodes, or by a reference-free workflow, which clusters and merges barcode sequences within a defined Levenshtein distance. Barcodes are then quantified per sample by read counts. For the single-cell workflow, raw paired-end datasets in fastq format (e.g., from targeted amplification of cellular barcodes within a single-cell library) or reads in BAM format after alignment to a reference genome (output as part of the pre-processing of scRNA-seq data) are taken as input. Barcode-containing reads are selected, quality filtered, and finally quantified by UMI counts using either a reference-based or reference- free approach. Outputs of all workflows include tables of processed counts with individual barcodes as rows and samples as columns, as well as a MultiQC run report including read filtering and alignment metrics. Tables of counts from BARtab, alongside associated sample metadata, can be imported into bartools for further downstream analysis and visualization. For single-cell datasets, barcode import interfaces directly with popular R-based single-cell frameworks including Seurat and SingleCellExperiment, resulting in cell-level annotation with lineage barcode metadata, which can be leveraged for analysis and visualization in bartools and other single-cell analysis packages.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Mouse: MLL-AF9 cell samples This study N/A Mouse: C57BL/6J spleen samples Fennell & Vassiliadis et al. 20229 N/A Chemicals, peptides, and recombinant proteins Dimethyl Sulfoxide (DMSO) Merck D4540 IBET-151 (GSK1210151A) Selleckchem S2780 Cytarabine (AraC) Merck PHR1787 Recombinant mouse IL-3 PeproTech 213–13 Recombinant human IL-6 PeproTech 200–06 Recombinant mouse SCF PeproTech 250–03 Viagen DirectPCR cell lysis reagent Viagen Biotech 301-C Proteinase K (20 mg/mL) Qiagen RP107B-1 Polybrene Merck TR-1003-G Polyethylenimine, Linear (PEI) Polysciences Inc. 23966–1 Critical commercial assays 10x Genomics Chromium Next GEM Single Cell 3ʹ Kit v3.1 10x Genomics PN-1000268 BGI Stereo-seq kit BGI Research V1.0 Deposited data Raw and analyzed data This study GEO: GSE246611 Processed stereo-seq spatial dataset at different bin sizes This study https://zenodo.org/records/10685805 Goyal et al. 2023 Nature single-cell lineage barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 https://figshare.com/articles/dataset/ FateMap_Paper_datasets_3_Goyal_ et_al_2021_Biorxiv_/22806494 Goyal et al. 2023 Nature single-cell cell barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 GEO: GSM7434409, GSM7434410, GSM7434411, GSM7434412 Fennel et al. 2022 Nature MLL-AF9 barcode-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912515 Fennel et al. 2022 Nature MLL-AF9 single-cell RNA-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912551 Experimental models: Cell lines Mouse: MLL-AF9 Fennell & Vassiliadis et al. 20229 N/A Experimental models: Organisms/strains Mouse: C57BL/6J The Jackson Laboratory 000664 Mouse: B6.SJL-Ptprca Pepcb/BoyJ The Jackson Laboratory 002014 Oligonucleotides Population based DNA barcode-seq library oligos Fennell & Vassiliadis et al. 20229 https://www.addgene.org/pooled-library/ dawson-splintr-libraries/ Recombinant DNA SPLINTR mCHERRY version 0 lentiviral barcode library Fennell & Vassiliadis et al. 20229 N/A SPLINTR BFP version 1 lentiviral barcode library Fennell & Vassiliadis et al. 20229 Addgene #179776 Software and algorithms Code repository for the study This study https://zenodo.org/doi/10.5281/ zenodo.10896642 (Continued on next page) Cell Reports Methods 4, 100763, May 20, 2024 e1

Techniques: High Throughput Screening Assay, Next-Generation Sequencing, Single-cell Analysis

Journal: Cell reports methods

Article Title: Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools.

doi: 10.1016/j.crmeth.2024.100763

Figure Lengend Snippet: Figure 2. Cellular barcoding QC analysis of the dose response dataset (A) Schematic of dose-escalation dataset experimental design. Drug-naive MLL-AF9 cells were transduced with a SPLINTR barcode-encoding lentiviral library in liquid culture, flow sorted for barcode expression, and expanded for 1 week in liquid culture. Following expansion, barcoded clones were split evenly into media containing either 400 nM IBET-151, 300 nM cytarabine (AraC), or 0.1% (v/v) DMSO (vehicle control) such that each treatment arm received an identical barcode repertoire. Cells were re-plated weekly in escalating concentrations of IBET-151, AraC, or vehicle. Alongside the dose escalation, the same pool of barcoded cells were seeded into an IC90 dose of IBET-151 (800 nM) or AraC (700 nM). Barcode sequencing was performed on samples obtained at the time points indicated. The experiment was performed in biological duplicate. For the AraC high-dose group, only time points 1 and 2 were sequenced due to low cell viability at later time points. (B) Replicate and global-level sample correlation analyses. Left: representative scatterplot of barcodes in technical replicates for the TP0 sample. Right: pairwise Pearson correlation heatmap of all biological replicate samples (treatment group indicated) in the dose-escalation dataset. (C) Total number of barcodes present at the 95th percentile in each sample following sample QC and merging of technical replicates. TR, technical replicate; TP, time point; TP0, time point 0/baseline sample.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Mouse: MLL-AF9 cell samples This study N/A Mouse: C57BL/6J spleen samples Fennell & Vassiliadis et al. 20229 N/A Chemicals, peptides, and recombinant proteins Dimethyl Sulfoxide (DMSO) Merck D4540 IBET-151 (GSK1210151A) Selleckchem S2780 Cytarabine (AraC) Merck PHR1787 Recombinant mouse IL-3 PeproTech 213–13 Recombinant human IL-6 PeproTech 200–06 Recombinant mouse SCF PeproTech 250–03 Viagen DirectPCR cell lysis reagent Viagen Biotech 301-C Proteinase K (20 mg/mL) Qiagen RP107B-1 Polybrene Merck TR-1003-G Polyethylenimine, Linear (PEI) Polysciences Inc. 23966–1 Critical commercial assays 10x Genomics Chromium Next GEM Single Cell 3ʹ Kit v3.1 10x Genomics PN-1000268 BGI Stereo-seq kit BGI Research V1.0 Deposited data Raw and analyzed data This study GEO: GSE246611 Processed stereo-seq spatial dataset at different bin sizes This study https://zenodo.org/records/10685805 Goyal et al. 2023 Nature single-cell lineage barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 https://figshare.com/articles/dataset/ FateMap_Paper_datasets_3_Goyal_ et_al_2021_Biorxiv_/22806494 Goyal et al. 2023 Nature single-cell cell barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 GEO: GSM7434409, GSM7434410, GSM7434411, GSM7434412 Fennel et al. 2022 Nature MLL-AF9 barcode-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912515 Fennel et al. 2022 Nature MLL-AF9 single-cell RNA-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912551 Experimental models: Cell lines Mouse: MLL-AF9 Fennell & Vassiliadis et al. 20229 N/A Experimental models: Organisms/strains Mouse: C57BL/6J The Jackson Laboratory 000664 Mouse: B6.SJL-Ptprca Pepcb/BoyJ The Jackson Laboratory 002014 Oligonucleotides Population based DNA barcode-seq library oligos Fennell & Vassiliadis et al. 20229 https://www.addgene.org/pooled-library/ dawson-splintr-libraries/ Recombinant DNA SPLINTR mCHERRY version 0 lentiviral barcode library Fennell & Vassiliadis et al. 20229 N/A SPLINTR BFP version 1 lentiviral barcode library Fennell & Vassiliadis et al. 20229 Addgene #179776 Software and algorithms Code repository for the study This study https://zenodo.org/doi/10.5281/ zenodo.10896642 (Continued on next page) Cell Reports Methods 4, 100763, May 20, 2024 e1

Techniques: Transduction, Expressing, Clone Assay, Control, Sequencing

Journal: Cell reports methods

Article Title: Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools.

doi: 10.1016/j.crmeth.2024.100763

Figure Lengend Snippet: Figure 4. Expressed cellular barcode pre-processing and analysis with BARtab and bartools (A) Histogram showing detected barcodes per cell in the single-cell dataset. Inset: pie chart showing the percentage of cells containing two or more barcodes that belong to clones represented by either a single cell (n = 1) or more than one cell (n > 1). n = number of cells. (B) Number of UMIs detected per cell and number of genes detected per cell are shown for cells with a barcode detected (red) or not detected (gray). (C) UMAP visualizations of the single-cell dataset following QC, filtering, and normalization showing Louvain clusters (left) and expressed barcode detection status (right). (D) Left: percentage of cells with a lineage barcode detected per 100 cells for each Louvain cluster. Right: median UMIs per cell for each Louvain cluster. (E) Dot plot showing the number of cells per lineage barcode. Clones comprising more than one lineage barcode are indicated. (F) Violin plots of number of UMI counts per clone (nCount_RNA) and number of genes detected per clone (nFeature_RNA) for the 16 clones represented by 100 or more cells in the single-cell dataset. (G) Histogram showing the percentage of cells comprising an exemplar clone, BC_1614, within each Louvain cluster. (H) Hypergeometric test results for enrichment of an exemplar clone, BC_1614, across Louvain clusters. Vertical line indicates log10 p value = 2. (I) UMAP visualization of the single-cell dataset with cells from selected clones highlighted.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Mouse: MLL-AF9 cell samples This study N/A Mouse: C57BL/6J spleen samples Fennell & Vassiliadis et al. 20229 N/A Chemicals, peptides, and recombinant proteins Dimethyl Sulfoxide (DMSO) Merck D4540 IBET-151 (GSK1210151A) Selleckchem S2780 Cytarabine (AraC) Merck PHR1787 Recombinant mouse IL-3 PeproTech 213–13 Recombinant human IL-6 PeproTech 200–06 Recombinant mouse SCF PeproTech 250–03 Viagen DirectPCR cell lysis reagent Viagen Biotech 301-C Proteinase K (20 mg/mL) Qiagen RP107B-1 Polybrene Merck TR-1003-G Polyethylenimine, Linear (PEI) Polysciences Inc. 23966–1 Critical commercial assays 10x Genomics Chromium Next GEM Single Cell 3ʹ Kit v3.1 10x Genomics PN-1000268 BGI Stereo-seq kit BGI Research V1.0 Deposited data Raw and analyzed data This study GEO: GSE246611 Processed stereo-seq spatial dataset at different bin sizes This study https://zenodo.org/records/10685805 Goyal et al. 2023 Nature single-cell lineage barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 https://figshare.com/articles/dataset/ FateMap_Paper_datasets_3_Goyal_ et_al_2021_Biorxiv_/22806494 Goyal et al. 2023 Nature single-cell cell barcode data (FM0-2 sample) Goyal et al. 2023 Nature30 GEO: GSM7434409, GSM7434410, GSM7434411, GSM7434412 Fennel et al. 2022 Nature MLL-AF9 barcode-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912515 Fennel et al. 2022 Nature MLL-AF9 single-cell RNA-seq data Fennell & Vassiliadis et al. 20229 GEO: GSM4912551 Experimental models: Cell lines Mouse: MLL-AF9 Fennell & Vassiliadis et al. 20229 N/A Experimental models: Organisms/strains Mouse: C57BL/6J The Jackson Laboratory 000664 Mouse: B6.SJL-Ptprca Pepcb/BoyJ The Jackson Laboratory 002014 Oligonucleotides Population based DNA barcode-seq library oligos Fennell & Vassiliadis et al. 20229 https://www.addgene.org/pooled-library/ dawson-splintr-libraries/ Recombinant DNA SPLINTR mCHERRY version 0 lentiviral barcode library Fennell & Vassiliadis et al. 20229 N/A SPLINTR BFP version 1 lentiviral barcode library Fennell & Vassiliadis et al. 20229 Addgene #179776 Software and algorithms Code repository for the study This study https://zenodo.org/doi/10.5281/ zenodo.10896642 (Continued on next page) Cell Reports Methods 4, 100763, May 20, 2024 e1

Techniques: Clone Assay

Journal: bioRxiv

Article Title: BRN2 and PTN unveil multiple neurodevelopmental mechanisms in Schizophrenia patient-derived cerebral organoids

doi: 10.1101/2021.06.10.447949

Figure Lengend Snippet: Briefly, two-dimensional iPSC colonies were dissociated, cultured into 3D embryoid body aggregates, differentiated towards a neuroectodermal fate, embedded in a Matrigel droplet to provide a matrix for expansion, and maturated upon an orbital shaker. b, Representative images of neural induction in 3D cerebral organoids. Shown is a representative image of a single ventricular zone from each group, exhibiting filamentous processes of neural stem cells (NESTIN+ cells), ventricular neural progenitors (SOX2+ cells), and their radially organized fibers which lead to dense neuronal fields (β-tubulin III; β3). c, Scz ventricular zone neural progenitors exhibit increased apoptosis. Analysis of co-localization of cleaved (activated) CAS3+ with SOX2+ cells revealed that neural progenitor apoptosis was significantly elevated in Scz organoids compared to Ctrl organoids (total n = 20 iPSC donors; Ctrl n = 5 and Scz n = 15). Scz organoids exhibited increased progenitor death irrespective of ventricular zone size and exhibited stable rates of error across ventricular zones (data not shown). In follow-up orthogonal validation experiments, increased death was replicated in an independent cohort via single-cell analysis of DNA fragmentation, DNA damage induction, and expression of cleaved PARP (see fig. S2). Together, these data establish progenitor entropy as an intrinsic Scz phenotype in cultures of 3D patient-derived neural tissue. d, Schematic of progenitor neuropathology in Scz organoids. Cortical development is defined by the sequential differentiation of neural progenitors into neurons that reside within the developing cortical plate. Due to a disruption in the survival of ventricular progenitors, we next assessed neuron numbers in Ctrl and Scz organoids. Scz organoids exhibited a decrease in the numbers of MAP2+ neurons relative to Ctrls (total n = 21 iPSC donors; Ctrl n = 4 and Scz n = 17). e, Neuronal differentiation is disrupted in Scz organoids. Lastly, we sought to determine if disrupted differentiation also contributes to neuronal depletion in Scz organoids. To label dividing progenitor cells and assess their fate, we used a 24hr BrdU pulse and 7d chase paradigm in a pseudorandomly selected cross- sectional cohort. Data is provided as a Sankey flow visualization so that differentiation can be evaluated as the proportion of all BrdU+ cells within a field. Approximately half of the BrdU+ cells differentiated into CTIP2+ early-born cortical neurons (48.6%) in Ctrl organoids. Contrary to this, Scz organoids exhibited a disrupted developmental trajectory whereby only 16.1% of cells differentiated into early-born CTIP2+ neurons (total n = 8 iPSC donors; n = 4 Ctrl and n = 4 Scz). While a trend for increased self- renewal of PAX6+ progenitors was detected in Scz organoids (43.7% BrdU+ cells) relative to Ctrls (34.4% BrdU+ cells), this comparison was not significant. Thus, in addition to progenitor death, Scz organoids exhibit fewer neurons in developing cortical fields due to a mechanism that restrained neurogenesis by altering the differentiation trajectory of neural progenitors. * p < 0.05, **** p < 0.0001. Error bars reflect Standard Error of the Mean. Scale bar: b and d = 60µm, whole organoid panel in c = 60µm, far right panel of c = 100µm. Ctrl: Control, Scz: Schizophrenia, VZ: Ventricular Zone.

Article Snippet: Primary antibodies comprised SOX2 (1:1000; R&D Systems, CAT#: MAB2018-SP), PAX6 (1:1000; Biolegend, CAT#: 901301), β-tubulin III (1:1000; Abcam, CAT#: AB41489), MAP2 (1:1000, Abcam, CAT#: AB11267; 1:1000, Abcam, CAT#: AB32454), BrdU (1:1000, BD Pharminogen, CAT#: 555627), DCX (1:1000, Santa Cruz, sc-271390), Activated/Cleaved Caspase-3 (1:1000; Cell Signalling Biology, CAT#: 9661S and Thermo Fisher Scientific, CAT#: 66470-2-IG), Nestin (1:1000, Abcam, CAT#: AB176571), pH3 (1:1000; Millipore, CAT#: 06-570), Ki67 (1:1000; BD Biosciences, CAT#: 550609), TBR2 (1:300; Abcam, CAT#: AB23345 and EMD Millipore, CAT#: AB15894), GFP (1:1000, Thermofisher, CAT#: A10262), CTIP2 (1:300; Abcam, CAT#: AB18465), and BRN2 (1:750, Santa Cruz, CAT#: SC-393324).

Techniques: Cell Culture, Biomarker Discovery, Single-cell Analysis, Expressing, Derivative Assay, Disruption, Comparison, Control

Journal: bioRxiv

Article Title: BRN2 and PTN unveil multiple neurodevelopmental mechanisms in Schizophrenia patient-derived cerebral organoids

doi: 10.1101/2021.06.10.447949

Figure Lengend Snippet: Briefly, whole organoids were subjected to tryptic digestion to isolate proteins from each sample. Proteins were next isobarically barcoded with TMT reagents, and then condensed into a single multiplexed suspension. This allowed samples to be run concurrently, eliminating batch-specific technical variance, and a low-noise assessment of both organoid variability (using a 4x2 design) and the identification of translated disease factors. Multiplexed suspensions were subsequently subjected to quantitative TMT-LC-MS for peptide detection, deconvolution, and quantification, and then computationally analyzed. b , Scz organoids recapitulate the developmental proteome. Given numerous cellular signatures of Scz that separated cases from Ctrls , we sought to determine the proteome diversity of Ctrl and Scz 3D organoids. The Scz organoid proteome was 99.95% identical in pooled protein diversity relative to Ctrls. Thus, Scz proteome differences are likely to be explained not in the induction of differential factors at the posttranslational level, but rather their total molecular quantity. c, Individual proteins exhibit low variability in expression between groups . Given the high degree of conservation in peptide diversity between both Ctrl and Scz organoids, we next examined dispersion of individual proteins by generating their coefficient of variation (CVs) for each group. In both Ctrl and Scz samples, over 3000 detectable peptides for each group exhibited low variability (<20% CVs; blue dots). This reflects that Ctrl and Scz organoids exhibited similar patterns of proteome reproducibility. d, Proteome-wide clustering separates Scz patient-derived organoids from Ctrls. A proteome-wide heat-map shows differences in LC-MS expression intensities between groups, which unveils clustering of differentially expressed proteins in Ctrl versus Scz organoids. Specifically, Scz organoids are defined by the differential expression of just 222 peptides, or ∼5.9% of the detected proteome. Thus, consistent with our hypothesis, Scz organoid samples principally differed in their quantity, rather than diversity, of developmental factors. e, Disease factors are detected and differentially expressed in Scz organoids. A heat-map of selected differentially expressed proteins in Scz organoids (Log2 key from panel f still applies). This included POU-domain peptide fragments that putatively mapped to the forebrain neuronal-development factor POU3F2 (BRN2), and disease factors with known and/or likely involvement in disease pathophysiology (e.g. COMT/PLCL1). Additionally, factors with putative genetic risk but otherwise unestablished disease biology (e.g. PTN) were also differentially expressed in Scz organoids. f, Schematic of pipeline for live single-cell RNA sequencing of organoids. Briefly, organoids from 4 Ctrl and 3 Scz lines were generated concurrently, pooled by- line, and dissociated to a single-cell suspension. We rapidly conducted survival-based high-throughput FACS to purify samples to 2000 live cells/µm per line. For robustness, post-FACS live cell viability was also cross-confirmed using Countess-II. Live cell suspensions were next rapidly loaded into 10x chromium microfluidic devices to produce barcoded single-cell nanodroplet emulsions. This emulsion was later broken, barcoded samples were amplified, libraries prepared, subjected to Illumina sequencing, and then parsed through a suite of unbiased computational analyses. g, Clustering of 26,335 transcriptomes recapitulates fetal brain cellular identities. Heatmap of marker genes of individual clusters identified via pairwise comparisons of the normalized expression values for cells of a given cluster vs. the cells of all other clusters. Shown here are unbiased gene sets, which define the top 10 gene markers for each cluster that met a high-pass FDR threshold of 1% and >15,000 total read counts. Many of these prototypic markers defined cell-types consistent with human fetal tissue [ , ] (see below). h, Resolving the identities of differentially enriched cell-types in Scz organoids. Shown are UMAP coordinates for 26,335 transcriptomes split by Ctrl and Scz cases. Cell-type labels were determined via a variety of approaches including marker gene- expression, automated annotation, and, namely, comparison with human fetal samples . Bar chart (right) depicts cell-type proportions, illustrating that ∼93% of Ctrl scRNA- Seq transcriptomes were identified as neural progenitors, proliferating cells, or terminal cortical cell-types (e.g. neurons and glia). Compared to this, only ∼75% of Scz cell-types exhibited a similar conservation of identity. Compared to remaining cell types in Ctrl organoids (∼7% cells), the remaining ∼25% of Scz scRNA-Seq transcriptomes reflected enrichment for brain-related cell-types including putative neuro-endothelial cells, structural markers, developing vasculature, retinal, and choroid plexus markers in Scz organoids. This analysis therefore revealed the cell-types produced at the expense of neurons in Scz organoids alluded to in pulse-chase experimentation depicted in . Of note, all cell-types exhibited reproducible proportions across individual iPSC donors within respective groups (i.e., all Scz organoids exhibited similarly reproducible alterations in cell-type diversity, which was defined by an overarching loss of neurons). i, Confirmation of progenitor and neuronal depletion in Scz organoids. Scz organoids exhibited a striking enrichment of the pluripotent stem cell marker POU5F1 (OCT4), which overlapped SOX2+ and PAX6 + progenitor UMAP coordinates. Correspondingly, Scz organoids exhibited depletion of progenitor markers ( SOX2 + and PAX6 +) as well as the pan-neuronal markers ( MAP2 +, DCX +, and STMN2 +). These expression differences reflect both abundance and magnitude. Thus, Scz organoids exhibit a deleterious increase in pluripotent cell-types (see POU5F1 /OCT4) in progenitor clusters, but an overall depletion of progenitors and neurons. For -j, total n = 26, 335 transcriptomes, n = 20,844 genes from 7 iPSC lines; Ctrl n = 15,089 transcriptomes from 4 Ctrl iPSC lines, and Scz n = 11,246 transcriptomes from 3 Scz iPSC lines. Ctrl: Control, Scz: Schizophrenia.

Article Snippet: Primary antibodies comprised SOX2 (1:1000; R&D Systems, CAT#: MAB2018-SP), PAX6 (1:1000; Biolegend, CAT#: 901301), β-tubulin III (1:1000; Abcam, CAT#: AB41489), MAP2 (1:1000, Abcam, CAT#: AB11267; 1:1000, Abcam, CAT#: AB32454), BrdU (1:1000, BD Pharminogen, CAT#: 555627), DCX (1:1000, Santa Cruz, sc-271390), Activated/Cleaved Caspase-3 (1:1000; Cell Signalling Biology, CAT#: 9661S and Thermo Fisher Scientific, CAT#: 66470-2-IG), Nestin (1:1000, Abcam, CAT#: AB176571), pH3 (1:1000; Millipore, CAT#: 06-570), Ki67 (1:1000; BD Biosciences, CAT#: 550609), TBR2 (1:300; Abcam, CAT#: AB23345 and EMD Millipore, CAT#: AB15894), GFP (1:1000, Thermofisher, CAT#: A10262), CTIP2 (1:300; Abcam, CAT#: AB18465), and BRN2 (1:750, Santa Cruz, CAT#: SC-393324).

Techniques: Suspension, Liquid Chromatography with Mass Spectroscopy, Expressing, Dispersion, Derivative Assay, Quantitative Proteomics, RNA Sequencing, Generated, High Throughput Screening Assay, Emulsion, Amplification, Illumina Sequencing, Marker, Gene Expression, Comparison, Produced, Pulse Chase, Control

Journal: bioRxiv

Article Title: BRN2 and PTN unveil multiple neurodevelopmental mechanisms in Schizophrenia patient-derived cerebral organoids

doi: 10.1101/2021.06.10.447949

Figure Lengend Snippet: Diffusion maps of single-cell differentiation trajectories were generated for all 26,335 cells via harmonized principal components. This revealed similar, but subtly altered, differentiation trajectories in Scz organoids (a). Heatmaps of slingshot-identified genes most-associated with pseudotime cell lineages of Scz organoids (b). Note differences in enrichment for collagen and matrix organization factors in Scz single-cell transcriptomes, whereas Ctrl organoids tended to exhibit up-regulation of neuronal factors (e.g. STMN2 , TUBB2B , CRABP1 ) as expected. Expression patterns of selected genes of interest, based on statistical thresholds, are projected onto diffusion map coordinates (c). Notably, markers such as CRABP1, IFITM3, and POU5F1 (OCT4) segregated Scz from Ctrl transcriptomes within differentiation trajectories (c), and were inappropriately enriched in progenitor cell-types and neuronal trajectories (see reference cell-type clusters in panel a). Therefore, these markers did not identify a broader pool of undifferentiated stem cells, but rather indicated a series of intrinsic alterations within pseudotime trajectories of Scz cell-types and their differentiation patterns. d-g, Scz progenitors exhibit disrupted transcription of axon development factors. SOX2 + progenitor scRNA-Seq transcriptomes were isolated and differentially expressed genes (DEG) examined. Heatmap denotes unbiased-discovery of downregulated DEG targets in Scz progenitors (d; 5% FDR threshold average logFC Scz/Ctrl ≤ 2.5, and >3 for up-regulated analysis in fig. S4d). This revealed down-regulation of a gene-set involved in axon development (e; see inset). Other DEG targets in Scz progenitors by biological process are presented here as a dotplot (f), resulted in gene-set enrichment for extracellular organization, adhesion, as well as angiogenesis in Scz progenitors. Gene ontology biological processes are shown in spoke-plot (g), and axon factors are enlarged in isobar-linked image for emphasis and evaluation (inset of e and g). h-k, Scz neurons exhibit pathway enrichment for growth factor binding. Similar to progenitors, neuron scRNA-Seq transcriptomes were examined for DEGs. Heatmap denotes unbiased-discovery of downregulated DEG targets in Scz neurons (h; 5% FDR threshold average LogFC Scz/Ctrl ≤ 2.5, and >3 for up-regulated analysis depicted in fig. S4e). Analysis of gene ontologies by molecular function revealed enrichment for growth factor binding in Scz neurons, which was defined by downregulated TrkA ( NTRK1 ) neurotrophin receptor gene expression and upregulation of factors such as FURIN (neurotrophin pro→mature processing), FGFR2 , PDGFA , TGFBR3, and the interleukin-6 signal transducer IL6ST (i-j). Protein-protein interactions between enriched growth factor binding factors in Scz neurons revealed notable interplay and interaction pathways (e.g. Furin→NGF→NTRK1) that place neuron-specific DEGs such as IL6ST and S100A13 as downstream targets of growth factor binding enrichment (k). Alternatively, these downstream factors may be putatively repressed by the inhibitory interaction effect of IGFBP3 on VEGFA (k), which are prototypic markers of neurovascular cells which were also upregulated in their abundance within Scz organoids (see ). Other pathway analysis (e.g. KEGG) identified altered Pl3K-Akt signaling in Scz neurons (see fig. S4g). l, Cell-specific disruption of neurotrophic factors and receptors in Scz organoids. Given unbiased detection of alterations in NT-3 ( NTF3 ) in Scz progenitors and TrkA ( NTRK1 ) receptors in Scz neurons, we next examined progenitor cell-types for specific differences in neurotrophins ( BDNF , BDNF-Antisense / BDNF-AS , NGF , NT-3 , and NT-4 ; left) and their cognate tyrosine kinase receptors ( TrkA , TrkB , TrkC , & p75 NTR/NGFR ; right). SOX2 + progenitors and radial glial cell (RGC) progenitors examined almost identical neurotrophin dysregulation and were defined by the trend for decreased expression of BDNF , BDNF-AS, NT-3, and TrkB expression. Curiously, NT-4 was upregulated in Scz progenitor cell-types. Scz neurons were defined only by their near 3- fold reduction of TrkA gene expression. m-n, Validating gene-expression differences of candidate proteome-derived targets, POU3F2 (BRN2) and PTN , in Scz organoids. Candidate proteome targets were cross-examined for differences at the single- cell level (see fig. S5 for expression of proteome targets in global, progenitor-only and neuron-only scRNA-Seq datasets). Given survival, differentiation, and diminished growth factor support, here we emphasize the neuronal transcription factor BRN2 ( POU3F2 ) and putative growth factor PTN as putative targets for mechanism experiments. Scz progenitors exhibited almost complete depletion of BRN2 relative to Ctrl progenitors, indicating disrupted induction of neuronal differentiation. PTN expression was disrupted in both Scz progenitors and neurons but exhibited a greater difference in progenitors. o, Resolving neural cell-type specificity of POU3F2 (BRN2) and PTN depletion in Scz organoids. Schematic summary of cell-specific alterations in Scz progenitors (left) and neurons (right). Scz progenitors exhibited entropy in neurotrophic growth factors, specific reduction of TrkB ( NTRK2 ), and depletion of axon development factors. Scz progenitors also exhibit enrichment for extracellular structure, matrix organization, and angiogenesis factors, which partially explains the intrinsic alteration in cell lineage ( -c) and differentiation trajectories towards neuro-endothelial factors and cell types ( and ). Total scRNA-Seq dataset comprised n = 26, 335 cells, n = 20,844 genes from 7 iPSC lines; Ctrl n = 15,089 scRNA-Seq transcriptomes from 4 Ctrl iPSC lines, and Scz n = 11, 246 scRNA-seq transcriptomes from 3 Scz iPSC lines. * significant p and FDR values. # = p value significant, with a final FDR score at the cut-off threshold. Ctrl: Control, Scz: Schizophrenia.

Article Snippet: Primary antibodies comprised SOX2 (1:1000; R&D Systems, CAT#: MAB2018-SP), PAX6 (1:1000; Biolegend, CAT#: 901301), β-tubulin III (1:1000; Abcam, CAT#: AB41489), MAP2 (1:1000, Abcam, CAT#: AB11267; 1:1000, Abcam, CAT#: AB32454), BrdU (1:1000, BD Pharminogen, CAT#: 555627), DCX (1:1000, Santa Cruz, sc-271390), Activated/Cleaved Caspase-3 (1:1000; Cell Signalling Biology, CAT#: 9661S and Thermo Fisher Scientific, CAT#: 66470-2-IG), Nestin (1:1000, Abcam, CAT#: AB176571), pH3 (1:1000; Millipore, CAT#: 06-570), Ki67 (1:1000; BD Biosciences, CAT#: 550609), TBR2 (1:300; Abcam, CAT#: AB23345 and EMD Millipore, CAT#: AB15894), GFP (1:1000, Thermofisher, CAT#: A10262), CTIP2 (1:300; Abcam, CAT#: AB18465), and BRN2 (1:750, Santa Cruz, CAT#: SC-393324).

Techniques: Diffusion-based Assay, Cell Differentiation, Generated, Expressing, Isolation, Binding Assay, Gene Expression, Protein-Protein interactions, Disruption, Derivative Assay, Control

Journal: Advanced materials interfaces

Article Title: Elucidating Extracellular Matrix and Stiffness Control of Primary Human Hepatocyte Phenotype Via Cell Microarrays

doi: 10.1002/admi.202101284

Figure Lengend Snippet: A) Single- and two-way combinations of 10 liver-inspired recombinant ECM proteins. Each circle represents a single protein (collagens 1 [C1], 3 [C3], 4 [C4], 5 [C5] decorin [DC], fibronectin [FN], hyaluronic acid [HA], laminin [LN], lumican [LC], and tenascin [TC]) and each grey line represents a two-way protein combination; a total of 55 compositions of ECM proteins were utilized in this study. B) Schematic of the experimental workflow, including: fabrication of microarrays using an automated microspotter to transfer pre-made ECM protein solutions to a polyacrylamide (PA) hydrogel of a tunable stiffness conjugated to a glass slide; seeding of PHHs that attach only to the ECM protein ‘islands’ and not to the PA gel; differential attachment of PHHs and expression of PHH markers on ECM islands as assessed via high-content imaging; plotting and statistical analysis of single cell imaging data across the various ECM conditions. C) Representative section of an ECM microarray demonstrating the differential attachment of PHHs to ECM islands of 450 µm diameter; nuclei are counterstained with DAPI and rhodamine-labeled dextran spots on microarrays are utilized for alignment. Scale bar = 500 µm. D) Representative islands of 4-color-stained PHHs on the microarrays of two different PA stiffnesses, 1 kPa and 25 kPa (Young’s moduli). Scale bars = 50 µm. E) Box plots showing the median number of cells per ECM island (center bar) along with the interquartile range on both microarray stiffnesses over 14 days of culture (each dot represents the mean cell number measured across islands of a specific ECM composition); mean distributions were compared across the stiffnesses using the Wilcoxon Test. **p<0.01 and ****p<0.0001. F) Heatmap of mean cell number across islands of single- (dashed line) and two-way combinations of the ECM proteins on the two stiffnesses after 14 days of culture. Similar heatmaps after 1 and 7 days of culture are shown in Figure S1A , Supporting Information . For panels E and F, data displayed represents summarized single-cell measurements across three PHH donors per condition (n = 22–24 ECM islands across representative microarrays).

Article Snippet: Furthermore, an OmniGrid Micro automated microspotter (Digilab, MA) created array grids of 10 ECM proteins in one- and two-way combinations, for a total of 55 combinations ( Figure 1A ).

Techniques: High Throughput Screening Assay, Recombinant, Expressing, Imaging, Single Cell, Microarray, Labeling, Staining