A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

Code to download and process this dataset is available in: https://github.com/seanome/2025-longevity-x-ai-hackathon Dataset structure is originally from AnnData. Descriptions of each data file is below.

  Data Files

This dataset contains multiple parquet files, one for each sheet in the original Excel file: gene-expression-single-cell-mouse_*.parquet - Data files containing gene expression and… See the full description on the dataset page: https://huggingface.co/datasets/longevity-db/gene-expression-single-cell-mouse.

The interplay between T- and B-cell compartments during naïve, effector and memory T cell maturation is critical for a balanced immune response. Primary B-cell immunodeficiency arising from X-linked agammaglobulinemia (XLA) offers a model to explore B cell impact on T cell subsets, starting from the thymic selection. Here we investigated characteristics of naïve and effector T cell subsets in XLA patients, revealing prominent alterations in the corresponding T-cell receptor (TCR) repertoires. We observed immunosenescence in terms of decreased diversity of naïve CD4+ and CD8+ TCR repertoires in XLA donors. The most substantial alterations were found within naïve CD4+ subsets, and we have investigated these in greater detail. In particular, increased clonality and convergence, along with shorter CDR3 regions, suggested narrower focused antigen-specific maturation of thymus-derived naïve Treg (CD4+CD45RA+CD27+CD25+) in the absence of B cells - normally presenting diverse self and commensal antigens. The naïve Treg proportion among naïve CD4 T cells was decreased in XLA patients, supporting the concept of impaired thymic naïve Treg selection. Furthermore, the naïve Treg subset showed prominent differences at the transcriptome level, including increased expression of genes specific for antigen-presenting and myeloid cells. Altogether, our findings suggest active B cell involvement in CD4 T cell subsets maturation, including B cell-dependent expansion of the naïve Treg TCR repertoire that enables better control of self-reactive T cells.

This archive provides all datasets needed to reproduce the single‐cell data integration detailed in the paper

Single-cell integration and multi-modal profiling reveals phenotypes and spatial organization of neutrophils in colorectal cancer

DOI: 10.1101/2024.08.26.609563

The archive comprises the following files:

• MUI_Innsbruck-adata.h5ad: In-house scRNA-seq dataset from CRC cohort I (n = 12) comprising matched peripheral blood, adjacent normal, and tumor samples generated using the BD Rhapsody platform.
• input_datasets.tar.xz: Preprocessed input datasets in .h5ad format required to build the CRC scRNA-seq atlas.
• crc_atlas_scanvi_model.tar.xz: Pretrained scArches reference model and matching .h5ad file (highly variable genes only), enabling projection of external data onto the CRC atlas.
• downstream_analyses_de_analysis.tar.xz: DESeq2-based differential expression analyses on pseudobulked data by cell type for various matched comparisons within the CRC atlas. Includes RDS files, result TSV tables, and short summaries for each comparison.

The CRC atlas is publicly available for download and interactive exploration through a cell-x-gene instance with standardized metadata, which allows custom analyses of the atlas. For more information, check out the

• project website and
• our github repository.

Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

It is https://en.wikipedia.org/wiki/ATAC-seq#Single-cell_ATAC-seq single cell ATAC-seq data. And single cell RNA-seq data: https://en.wikipedia.org/wiki/Single-cell_transcriptomics#Single-cell_RNA-seq

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

(For companion dataset on CITE-seq = scRNA-seq + Proteomics, see: https://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021)

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Background: In the event of an improvised nuclear device detonation, the prompt radiation exposure would consist of γ rays plus a neutron component that would contribute to the total dose. As neutrons cause more complex and difficult to repair damage to cells that would result in more severe health burden to affected individuals, it is paramount to be able to estimate the contribution of neutrons to an estimated dose, to provide information for those making treatment decisions. Results: Mice exposed to either 0.25 or 1 Gy of neutron or 1 or 4 Gy x-ray radiation were sacrificed at 1 or 7 days after exposure. Whole genome microarray analysis identified 7,285 and 5,045 differentially expressed genes in the blood of mice exposed to neutron or x-ray radiation, respectively. Neutron exposure resulted in mostly downregulated genes, whereas x-rays showed both down- and up-regulated genes. A total of 34 differentially expressed genes were regulated in response to all ≥1 Gy exposures at both times. Of these, 25 genes were consistently downregulated at days 1 and 7, whereas 9 genes, including the transcription factor E2f2, showed bi-directional regulation; being downregulated at day 1, while upregulated at day 7. Gene ontology analysis revealed that genes involved in nucleic acid metabolism processes were persistently downregulated in neutron irradiated mice, whereas genes involved in lipid metabolism were upregulated in x-ray irradiated animals. Most biological processes significantly enriched at both timepoints were consistently represented by either under- or over-expressed genes. In contrast, cell cycle processes were significant among down-regulated genes at day 1, but among up-regulated genes at day 7 after exposure to either neutron or x-rays. Cell cycle genes downregulated at day 1 were mostly distinct from the cell cycle genes upregulated at day 7. However, five cell cycle genes, Fzr1, Ube2c, Ccna2, Nusap1, and Cdc25b, were both downregulated at day 1 and upregulated at day 7. Conclusions: We describe, for the first time, the gene expression profile of mouse blood cells following exposure to neutrons. We have found that neutron radiation results in both distinct and common gene expression patterns compared with x-ray radiation. Overall design: Radiation induced gene expression in mouse blood was measured at 1, 3, and 7 days after 0.25 Gy and 1 Gy neutron exposure or after 1 Gy and 4 Gy of x-rays. Six independent experiments were performed at each dose and time point.

We sequenced the mRNAs of embryonic stem cells (ESCs) cultured in different conditions. The two lines M (male) and F (female) used in this study were derived from E4 blastocysts of the same cross between a C57BL/6J (Mus musculus domesticus) and CAST/EiJ (Mus castaneus) male. mESCs were cultured in 2i and LIF as the ground state condition or in serum and LIF as the conventional condition. Epistem cell lines were also generated from the two lines by culturing them with Activin A and FGF2. In order to study more advanced development, we differentiated the two mESC lines through embryonic body formation to postmitotic motor neurons using retinoic acid and the smoothened agonist SAG. This differentiation process also results in the derivation of several types of interneurons. We picked single cells from all different conditions and generated sequencing libraries using the Smart-seq2 and Tn5 protocol. For simplicity, we designate the different condition as ES2i, ES, Epi and Neuron from hereon. We also obtained preimplantation inner cell mass and epiblast cells from E3.5 ICM (inner cell mass) and E4.5 blastocysts of the crossbred mice (male CAST/EiJ × female C57BL/6J) as well as postimplantation epiblast cells from E5.5 embryos of C57BL/6J mice Examination of gene expression profile in individual male and female embryonic stem cell lines along developmental progression

The Single-cell Analysis Market size was valued at USD 4.89 billion in 2023 and is projected to reach USD 16.24 billion by 2032, exhibiting a CAGR of 18.7 % during the forecasts period. Recent developments include: In February 2024, 10X Genomics announced the launch of GEM-X, comprising two single-cell gene assays-Chromium Single Cell Gene Expression 3'v4 and Chromium Single Cell Immune Profiling 5'v3, helping 10X Genomics to expand its single-cell technology products portfolio. , In February 2024, Takara Bio USA, Inc announced the launch of two single-cell solutions, Shasta Total RNA-Seq Kit and Shasta Whole-Genome Amplification Kit. , In February 2024, Singleron Biotechnologies announced the opening of its labs in Ann Arbor, Michigan, U.S. The company planned to offer single-cell analysis service, comprehensive solutions from tissue dissociation, single-cell multi-omic analysis, single cell reagent kits, automation instruments, to bioinformatics support. , In January 2024, BD announced a collaboration with Hamilton Ink, a robotics developer organization, to support the creation of automated solutions for single-cell multiomics research purposes. , In January 2024, Singleron Biotechnologies launched AccuraSCOPE Single Cell Transcriptome and Genome Library Kit at the Festival of Genomics meeting in the UK. This innovative kit can simultaneously profile full genome and full-length transcriptome, offering researchers a valuable tool for their studies. , In September 2023, Illumina, Inc. collaborated with Singleron Biotechnologies for an optimized workflow that automatically initiates DRAGEN single cell RNA sequencing analysis following the sequencing of a Singleron GEXSCOPE single-cell library using an Illumina NextSeq 2000 system. , In April 2023, Fluidigm Corporation (Standard BioTools Inc.) launched the Hyperion XTi Imaging System. The XTi delivers high-precision imaging and quantification of complex biological information at the single-cell level. .

The aim of the study was to investigate X-inactivation during human T-cell development in the thymus. Thymocytes from six different developmental stages from nine individuals were isolated and studied regarding gene expression. The data set contains data from RNA sequencing, DNA methylation and whole exome sequencing.

The dataset contains data from the manuscript "A landscape of X-inactivation during human T-cell development in the thymus" by Gylemo et al, and consists of results from RNA sequencing (fastq.gz), analysis of DNA methylation (Grn.idat), Smart-seq2 single-cell RNA sequencing (fastq.gz) and whole exome DNA sequencing (fastq.gz) of six developmental stages of thymocytes from nine individuals.

The data is described on individual and cell-type level in the tab-separated metadata files (.tsv). MD5 checksums for each file are included.

The scoring method used for selection of canonical pathways was Fisher’s Exact Test. The ration (r) is calculated by the number of genes involved and diving by the total number of genes in that canonical pathway in IPA.

Mononuclear cells were collected from synovial fluid of anti-citrullinated positive antibodies (ACPA)-positive rheumatoid arthritis patients (n=8) and ACPA-negative RA patients (n=8). Global cell types (i.e., no enrichment) were obtained from these cryopreserved synovial fluid mononuclear cells (SFMC) samples and immediately fixed and processed using the GEM-X Flex Gene Expression Reagent Kits (10x Genomics) according to protocol. Following Gel Bead-in Emulsion (GEM) generation, samples were processed using the standard manufacturer’s protocol. Once sequencing libraries passed standard quality control metrics, libraries were sequenced on an Illumina NextSeq2000 P4 100 cycle reagent kit with the following read structure: R1: 28, R2: 90, I1: 10, I2: 10. Libraries were sequenced to obtain a read depth greater than 10,000 reads/cell for gene-expression (GEX). FASTQ files are made available. More detailed information can be obtained in Argyriou A. et al, Annals of the Rheumatic Disease, 2025.

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev (Scanpy is not always reliable for cell cycle analysis ).

https://scanpy.readthedocs.io/en/stable/

Scanpy – Single-Cell Analysis in Python

Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.

Single cell RNA sequencing data - count matrices: rows - correspond to cells, columns to genes, value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with SCANPY, we present ANNDATA, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).

Paper:

Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). https://doi.org/10.1186/s13059-017-1382-0 https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1382-0

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6 Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Yeast single-cell gene expression data, database-derived prior knowledge network, and hand-curated gold standard network. Used to benchmark the Inferelator 3.0, SCENIC, and CellOracle.

Expression data (GSE144820_GSE125162.tsv.gz) is an integer count matrix [44343 rows x 6763 columns] with an index column (0) assembled from GSE144820 and GSE125162. Included is a paired metadata file (GSE144820_GSE125162_META_DATA.tsv.gz).

A database-derived prior knowledge network (YEASTRACT_20190713_BOTH.tsv) is a boolean connectivity matrix [6885 rows x 220 columns] with an index column (0) obtained from the YEASTRACT database on 07132019. It consists of edges which have both DNA localization evidence and evidence of changes to gene expression after TF perturbation.

A curated gold standard network (Tchourine_2018_yeast_gold_standard.tsv) is a signed connectivity matrix [993 rows x 98 columns] with an index column (0). Details of its construction have been published.

Neurodegenerative diseases such as Alzheimer's disease exhibit pathological changes in the brain that proceed in a stereotyped and regionally specific fashion. However, the cellular underpinnings of regional vulnerability are poorly understood, in part because whole-brain maps of a comprehensive collection of cell types have been inaccessible. Here, we deployed a recent cell-type mapping pipeline, Matrix Inversion and Subset Selection (MISS), to determine the brain-wide distributions of pan-hippocampal and neocortical cells in the mouse, and then used these maps to identify general principles of cell-type-based selective vulnerability in PS19 mouse models. We found that hippocampal glutamatergic neurons as a whole were significantly positively associated with regional tau deposition, suggesting vulnerability, while cortical glutamatergic and GABAergic neurons were negatively associated. We also identified oligodendrocytes as the single most strongly negatively associated cell type. Furt..., Gene expression The scRNAseq data used to generate the cell-type maps come from Yao, et al. for the Allen Institute for Brain Science (AIBS), which sequenced approximately 1.3 million individual cells sampled comprehensively throughout the neocortex and hippocampal formation at 10x sequencing depth (Yao et al, 2021, Cell). Using a standard Jaccard-Louvain clustering algorithm, the authors jointly and hierarchically clustered these samples at three taxonomic levels: class (n = 4), subclass (n = 42), and cluster (n = 387). The full annotation and gene expression profile of each sample, as well as trimmed mean expression across cell-type clusters, are publicly available (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x). Here we used this trimmed means by cluster dataset, as the Matrix Inversion and Subset Selection (MISS) algorithm only requires the consensus profiles of cell types per cluster. Utilizing the hierarchical taxonomy provided by the ..., , # Searching for the cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer's disease

https://doi.org/10.5061/dryad.h18931zwv

Description of the data and file structure

Files and variables

File: Yao_Dependencies.mat

Description:Â Contains all cell density and gene expression files required to run the analyses in the paper.

Variables

• classkey: 42 x 1 cell array of Yao cell-type names
• gene_names: 3763 x 1 cell array of gene symbols for the intersection set of genes between the scRNAseq (Yao et al, 2021, Cell) and ISH (Lein et al, 2007, Nature) datasets
• geneinds: 1 x 3763 numeric array of MRx3-reordered gene indices in "gene_names" (see Mezias et al, 2022, PNAS and https://github.com/Raj-Lab-UCSF/MISS-Pipeline)
• genevct: 3763 x 42 numeric array of cell-type-specific gene expression across all 42 Yao cell types and 3763 inters...

This dataset contains fastq-files from single cell 5' RNA sequencing of the AML cell line HNT34 and normal T cells following co-culture with and without an antibody blocking SLAMF6 (TNC-1). The libraries were prepared using 10X GEM-X Universal 5' Gene Expression v3 Reagent Kit. In total, the dataset contains sequenced gene expression libraries from four samples (HNT34 co-cultured with T cells from two different donors; for both donors there is one sample with and one sample without the blocking antibody).

The transcription factor STAT5 plays a critical role in B cell acute lymphoblastic leukemia (B-ALL). How STAT5 mediates this effect is unclear. Here we demonstrate that STAT5 activation cooperates with defects in the pre-BCR signaling components encoded by Blnk, Btk, Prkcb, Nfkb1, and Ikzf1 to initiate B-ALL. STAT5 antagonizes NF-κB and IKAROS by opposing regulation of shared target genes. STAT5 binding was enriched at super-enhancers, which were associated with an opposing network of transcription factors, including PAX5, EBF1, PU.1, IRF4, and IKAROS. Patients with high ratios of active STAT5 to NF-κB or IKAROS have more aggressive disease. Our studies illustrate that an imbalance of two opposing transcriptional programs drive B-ALL, and suggest that restoring the balance of these pathways may inhibit B-ALL. Overall design: Gene expression profiling was performed on cells isolated from lymph nodes of Stat5b-CA, Stat5b-CA x Blnk+/-, Stat5b-CA x Xid-/-, Stat5b-CA x Pkrcb-/-, Stat5b-CA x Ebf1+/- and Stat5b-CA x Rag2-/- leukemic mice and pre B cells sorted from bone marrow of C57BL/6 wild type mice, Xid Mice, and Stat5b-CA non-leukemic mice. 40 Samples.

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

CITE-seq - joint single cell RNA sequencing + single cell measurements of CD** proteins. (https://en.wikipedia.org/wiki/CITE-Seq) (For companion dataset on scRNA-seq + scATAC-seq, see: https://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021 )

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Klinefelter syndrome (KS) is the most prevalent aneuploidy in males and is characterized by a 47,XXY karyotype. Less frequently, higher grade sex chromosome aneuploidies (HGAs) can also occur. Here, using a paradigmatic cohort of KS and HGA induced pluripotent stem cells (iPSCs) carrying 49,XXXXY, 48,XXXY, and 47,XXY karyotypes, we identified the genes within the pseudoautosomal region 1 (PAR1) as the most susceptible to dosage- dependent transcriptional dysregulation and therefore potentially responsible for the progressively worsening phenotype in higher grade X aneuploidies. By contrast, the biallelically expressed non-PAR escape genes displayed high interclonal and interpatient variability in iPSCs and differentiated derivatives, suggesting that these genes could be associated with variable KS traits. By interrogating KS and HGA iPSCs at the single-cell resolution we showed that PAR1 and non-PAR escape genes are not only resilient to the X-inactive specific transcript (XIST)-mediated inactivation but also that their transcriptional regulation is disjointed from the absolute XIST expression level. Finally, we explored the transcriptional effects of X chromosome overdosage on autosomes and identified the nuclear respiratory factor 1 (NRF1) as a key regulator of the zinc finger protein X-linked (ZFX). Our study provides the first evidence of an X-dosage-sensitive autosomal transcription factor regulating an X-linked gene in low- and high-grade X aneuploidies.

MYC associated transcriptional regulator X Enables DNA-binding transcription factor activity; identical protein binding activity; and sequence-specific DNA binding activity. Involved in several processes, including cellular response to peptide hormone stimulus; regulation of gene expression; and response to insulin. Located in PML body and dendrite. Human ortholog(s) of this gene implicated in lung small cell carcinoma and pheochromocytoma. Orthologous to human MAX (MYC associated transcriptional regulator X). Enables DNA-binding transcription factor activity; identical protein binding activity; and sequence-specific DNA binding activity. Involved in several processes, including cellular response to peptide hormone stimulus; regulation of gene expression; and response to insulin. Located in PML body and dendrite. Human ortholog(s) of this gene implicated in lung small cell carcinoma and pheochromocytoma. Orthologous to human MAX (MYC associated factor X); PARTICIPATES IN mitogen activated protein kinase signaling pathway; small cell lung carcinoma pathway; INTERACTS WITH 17beta-estradiol; 2,3,7,8-tetrachlorodibenzodioxine; 2,4-dinitrotoluene.

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: https://pubmed.ncbi.nlm.nih.gov/30104629/ Nat Immunol. 2018 Sep;19(9):1013-1024. doi: 10.1038/s41590-018-0181-4. Epub 2018 Aug 13. Human germinal center transcriptional programs are de-synchronized in B cell lymphoma Pierre Milpied 1, Iñaki Cervera-Marzal 2, Marie-Laure Mollichella 2, Bruno Tesson 3, Gabriel Brisou 2, Alexandra Traverse-Glehen 4, Gilles Salles 4, Lionel Spinelli 2, Bertrand Nadel 5

Data in two variants: 1) Downloaded from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3190075 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3190076 2) Downloaded from PanglaoDB: https://panglaodb.se/view_data.php?sra=SRA721637&srs=SRS3416994

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell