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Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses

Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses" # README ## Introduction This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages. ## Directory structure and execution details ### R code - Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory. - All figures will be saved within the `r_code/code_generated_figures` directory. - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script. ### MATLAB code - Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory. - All figures will be saved within the `matlab_code/code_generated_figures` directory. - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs). ### Python code - Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory. - All figures will be saved within the `python_code/code_generated_figures` directory. - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`. ## Supplementary code (for reference only as raw data is not included) ### Mapping code and genome construction code - Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:   `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs. - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:   `python_code/mapping_and_genome_construction/campari2_genome_construction.py`. - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:   `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`. - A custom genome was constructed to account for the expression of various artificial promoter viruses:   `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.

Mesothelial cells exhibit characteristics of perivascular cells in an in vitro angiogenesis assay

Supplementary Movie 1: Movie showing mesothelial cell interaction with endothelial cells during network formation.

Hepatitis B surface antigen reduction is associated with hepatitis B core-specific CD8+ T cell quality

The file containing post QC, count matrix, containing 6 samples as following. (S01:CHBN001, S02:CHBN002, S03:CHBN003, S04:CHBN004, S12:CHBN005, S14:CHBN006.) scRNAseq Libraries generated by 10xGenomics 5'-kit were read by NovaSeq 6000 platform.  After sequencing, raw reads were mapped to human generated by cellranger 6.1.2, then generated count matrix were subjected to QC according to Seurat manual (mitochondrial genes <10%, ribosomal genes > 0.05%), then SCT-transformed and integrated with 3000 features. Detail of QC/integration will be described in our manuscript.

opencc-by-4.0Apr 2023View details →

Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS

Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS". It includes the output of SCINPAS, available here: <a href="https://github.com/zavolanlab/SCINPAS">https://github.com/zavolanlab/SCINPAS</a>, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.

opencc-by-4.0Jul 2023View details →

Discrete Flow Cytometry of Underway Samples from TN414 Using a BD Influx Cell Sorter

The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the transit from Fiji to New Zealand (TN414) in the South Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton

Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell

NetCDF files for figures in "Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell" submitted to Journal of Advances in Modeling Earth Systems. Figure 1: Prescribed SST for control and perturbed simulations:  <ul> <li>sst_ctrl_annualzonalmeans.nc</li> <li>sst_pert_annualzonalmeans.nc</li> </ul> Figure 2: Mass meridional stream function data for control and perturbed simulations: Figure 2a: PC2 cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_pc2gr_ctrl_annualmeans.nc</li> <li>mmsf_pc2gr_pert_annualmeans.nc</li> </ul> Figure 2b: Smith cloud scheme + Gregory-Rowntree mass-flux convection scheme <ul> <li>mmsf_smithgr_ctrl_annualmeans.nc</li> <li>mmsf_smithgr_pert_annualmeans.nc</li> </ul> Figure 3a: Ice cloud fraction data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3b: Ice cloud fraction data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>Cf_smithgr_ctrl_annualzonalmeans.nc</li> <li>Cf_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3c: Relative humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_pc2gr_ctrl_annualzonalmeans.nc</li> <li>RH_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3d: Relative humidity for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>RH_smithgr_ctrl_annualzonalmeans.nc</li> <li>RH_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3e: Specific humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_pc2gr_ctrl_annualzonalmeans.nc</li> <li>q_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3f: Specific humidity data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>q_smithgr_ctrl_annualzonalmeans.nc</li> <li>q_smithgr_pert_annualzonalmeans.nc</li> </ul> Figure 3g: Temperature data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_pc2gr_ctrl_annualzonalmeans.nc</li> <li>T_pc2gr_pert_annualzonalmeans.nc</li> </ul> Figure 3h: Temperature data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years) <ul> <li>T_smithgr_ctrl_annualzonalmeans.nc</li> <li>T_smithgr_pert_annualzonalmeans.nc</li> </ul>  Figure 5: Control and perturbed simulation data for ice cloud fraction and mass fraction of ice cloud condensates meaned over the tropical ascent region for PC2 scheme <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 6a,b: Monthly-mean data of convective mass flux for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>convmassflux_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>convmassflux_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6c,d: Monthly-mean data of net convective detrainment for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>netconvdetrain_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 6e,f: Monthly-mean data of tendency of ice cloud condensates due to convection for control and perturbed simulations over the tropics between \(\pm\) 31 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtconv_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 7: Annual and zonal-mean data of net convective detrainment for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 8: Annual and zonal-mean data of net convective detrainment and ice cloud fraction for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 9: Annual and zonal-mean data of convective tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtconv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtconv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 10: Annual and zonal-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtnetdep_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul>  Figure 11: Monthly-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropics between \(\pm\) 31\(^\circ\)N for PC2 cloud scheme <ul> <li>dqcfdtnetdep_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtnetdep_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul>  Figure 12: Annual and zonal-mean data of specific humidity and advective tendency for specific humidity for control and perturbed simulations over the tropical ascent regoin between \(\pm\) 7 \(^\circ\)N for PC2 cloud scheme <ul> <li>q_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>q_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> Figure 13: Control and perturbed simulation data for mean vertical advection of moisture, mean vertical moisture convergence, total advection of moisture, and total moisture convergence (meaned over the tropical ascent region for PC2 scheme for 15 simulation years) <ul> <li>MeanVerticalAdvectionOfMoisture_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalAdvectionOfMoisture_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> </ul>

Circulating hybrid cell dataset

Peripheral blood mononuclear cell images for each region of interest (ROI). Each ROI .zip contains 3-channel, 96 by 96 pixel (0.325 mpp) single-cell .tiffs following the naming convention: "CG_{}_R1_{}_R2_{}_LABEL_{}_CENTROID_{}_{}.tiff". CG is the computational gating circulating hybrid cell call ("positive", "near", or "negative"), R1 is the number of positive votes received in annotation round 1 (0-7), R2 is the number of positive votes received in annotation round 2 (0-10), label is the cell's segmentation mask label, and centroid is the x/y coordinates of the cell centroid in the ROI. The marker order is [epithelial cocktail, CD45, DAPI].

Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks

This repository contains training, validation, and testing of fluorescence image data sets with their label for spheroid cell death classification.  These data are intended to be used in the paper "Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks" currently submitted

opencc-by-4.0Dec 2022View details →

Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients

R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes

opencc-by-4.0Apr 2023View details →

Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets

Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals: Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019). Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020). Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020). Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018). Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

dryad40/100

Data and code for: Spatial cell type enrichment predicts mouse brain connectivity

A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

opencc-zeroAug 2023View details →

opencc-by-4.0Mar 2023View details →

Stiffness transitions in new walls post-cell division differ between Marchantia polymorpha gemmae and Arabidopsis thaliana leaves

Plant morphogenesis is governed by the mechanics of the cell wall–a stiff and thin polymeric box that encloses the cells. The cell wall is a highly dynamic composite material. New cell walls are added during cell division. As the cells continue to grow, the properties of cell walls are modulated to undergo significant changes in shape and size without breakage. Spatial and temporal variations in cell wall mechanical properties have been observed. However, how they relate to cell division remains an outstanding question. Here we combine time-lapse imaging with local mechanical measurements via atomic force microscopy to systematically map the cell wall’s age and growth, with their stiffness. We make use of two systems, M. polymorpha gemmae, and A. thaliana leaves. We first characterise the growth and cell division of M. polymorpha gemmae. We then demonstrate that cell division in M. polymorpha gemmae results in the generation of a temporary stiffer and slower growing new wall. In contrast, this transient phenomenon is absent in A. thaliana leaves. We provide evidence that this different temporal behaviour has a direct impact on the local cell geometry via changes in the junction angle. These results are expected to pave the way for developing more realistic plant morphogenetic models and to advance the study into the impact of cell division on tissue growth.

Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. Please refer to the READMEs in the directories, which are summarized below. The record contains the following files: `clusters.tsv`:  contains the cluster id, name and colour of clusters in the paper scATAC.zip Analysis products for the single-cell ATAC-seq data. Contains: - `cells.tsv`: list of barcodes that pass QC. Columns include:     - `barcode`     - `sample`: (time point)     - `umap1`     - `umap2`     - `cluster`     - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root     - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `features.tsv`: 50 dimensional representation of each cell  - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`  scATAC_clusters.zip Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data.  - `clusters.tsv`: contains the cluster id, name and colour used in the paper - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format. - `fragments`: contains per cluster fragment files  scATAC_scRNA_integration.zip Analysis products from the integration of scATAC with scRNA. Contains: - `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45. - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type. - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode. scRNA.zip Analysis products for the single-cell RNA-seq data. Contains: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC) - `genes.txt`: list of all genes - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC)      - `sample`: sample name (D0, D2, .., D14, iPSC)     - `umap1`     - `umap2`     - `nCount_RNA`     - `nFeature_RNA`     - `cluster`     - `percent.mt`: percent of mitochondrial transcripts in cell     - `percent.oskm`: percent of OSKM transcripts in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`  - `pca.tsv`: first 50 PC of each cell - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv` multiome.zip multiome/snATAC: These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2).  - `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:     - `barcode`     - `umap1`: These are the coordinates used for the figures involving multiome in the paper.     - `umap2`: ^^^      - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq     - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels.  - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`. - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`. - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`. multiome/snRNA: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency. - `genes.txt`: list of all genes (this is different from the list in scRNA analysis) - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively)      - `sample`: sample name (D1M, D2M)     - `nCount_RNA`     - `nFeature_RNA`     - `percent.oskm`: percent of OSKM genes in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`

MarrowDLD: a microfluidic method for label-free retrieval of fragile bone marrow cells

We introduce here a label-free cytometry microsystem, MarrowDLD, based on deterministic lateral displacement. MarrowDLD enables the isolation of fragile cells based on intrinsic size properties while preserving their viability and functionality. Bone marrow adipocytes, obtained from mouse and human stromal line differentiation, as well as megakaryocytes, from primary human CD34+ hematopoietic stem and progenitor cells, were used for validation.

Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser

In our publication, we created simulations of paired heavy and light chain BCR sequences. Uploaded here is all the data needed to rerun the simulations, as well as the output of the simulations we ran. The output of BCR phylo included here are the lineage trees, unpickled and put into one file (true_trees.tsv), and the fasta files ('starting_fastas' folder). The naive BCR sequences we used as a starting point are found in the 'naive_data' folder. The post-simulation data for all 20 iterations (combined heavy and light chain data through both simulation frameworks) can be found in the 'simulation_data' folder.

Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study - Supporting Dataset

The cycling and impedance data used in the work "Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study" are provided in this repository. The data are reported both as original .txt files with the raw data from the instruments and as processed Matlab files, where the data are structured in cycles. The Excel sheet "Metadata" explains the type of battery associated to each code and file(s).

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (wtChR2-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ChR2WT fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   [1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52. [2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (ChR2(C34A/C36A)-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ChR2C34A/C36A fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   [1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52. [2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (b1AR-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ß1AR fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (CD28-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing CD28 fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>