B / Plasma Cell

Download processed single-cell RNA-seq data for B lymphocytes and plasma cells

Dataset Information

Cell Type

B / Plasma

Total Cells

243,734 cells

Sample Count

536 samples

Subpopulation Count

15 subpopulations

Download Resources

Raw Data Files

Complete single-cell RNA sequencing data in standard 10X Genomics format

10X Raw Data Package

File: Bcell_10x_raw_package.zip

Format: ZIP

Description: Contains barcodes/features/matrix/metadata in standard 10X format

Download
Zenodo

Barcode File

File: Bcell_barcodes.tsv.gz

Format: TSV.GZ

Description: Contains cell barcode information for each cell in the dataset.

Feature File

File: Bcell_features.tsv.gz

Format: TSV.GZ

Description: Gene features and annotations including gene symbols and IDs.

Matrix File

File: Bcell_matrix.mtx.gz

Format: MTX.GZ

Description: Sparse expression matrix containing gene expression counts.

Metadata File

File: Bcell_metadata.csv.gz

Format: CSV.GZ

Description: Cell-level metadata including annotations and sample information.

Marker Genes

Marker lists and published genes are uploaded in Zenodo：scHNDB-Cell Type-Marker Genes（DOI 10.5281/zenodo.19535753）。

All markers (each subpopulation)

File: B_Plasma_all_Markers_of_each_subpopulation.xls

Description: All markers of each subpopulation

Download (China Mirror OSS)

Top10 markers (each subpopulation)

File: B_Plasma_top10_Markers_of_each_subpopulation.xls

Description:Top10 marker of each subpopulation

Download (China Mirror OSS)

Published reference gene sets

File: B_Plasma_cell_ref_geneset.zip

Description: Published gene sets (zip)

Download (China Mirror OSS)

Key Marker Genes for B / Plasma Cells

  • Pan B cell markers: CD19, MS4A1 (CD20), CD79A, CD79B
  • Germinal center B cells: BCL6, AICDA, CD38
  • Plasma cells: MZB1, JCHAIN, SDC1 (CD138), XBP1
  • Antibody secretion: IGHA1, IGHG1, IGKC
  • Class switching / activation: AID (AICDA context), PRDM1 (Blimp-1)

Usage Instructions

These files are compatible with standard single-cell analysis tools. To load the data:

In R (Seurat):

library(Seurat)
data <- Read10X(data.dir = "path/to/extracted/files")
seurat_obj <- CreateSeuratObject(counts = data)

In Python (Scanpy):

import scanpy as sc
adata = sc.read_10x_mtx('path/to/extracted/files')