Foundry120 atlas

Single-cell RNA sequencing of ovarian, colorectal and breast cancer

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Dataset overview

Participants None
Samples 49
Reuse readiness 6.4/10 evidence-backed score

Uncovering NK cell sabotage in gut diseases via single cell transcriptomics.

Abstract

The identification of immune environments and cellular interactions in the colon microenvironment is essential for understanding the mechanisms of chronic inflammatory disease. Despite occurring in the same organ, there is a significant gap in understanding the pathophysiology of ulcerative colitis (UC) and colorectal cancer (CRC). Our study aims to address the distinct immunopathological response of UC and CRC. Using single-cell RNA sequencing datasets, we analyzed the profiles of immune cells in colorectal tissues obtained from healthy donors, UC patients, and CRC patients. The colon tissues from patients and healthy participants were visualized by immunostaining followed by laser confocal microscopy for select targets. Natural killer (NK) cells from UC patients on medication showed reduced cytotoxicity compared to those from healthy individuals. Nonetheless, a UC-specific pathway called the BAG6-NCR3 axis led to higher levels of inflammatory cytokines and increased the cytotoxicity of NCR3+ NK cells, thereby contributing to the persistence of colitis. In the context of colorectal cancer (CRC), both NK cells and CD8+ T cells exhibited significant changes in cytotoxicity and exhaustion. The GALECTIN-9 (LGALS9)-HAVCR2 axis was identified as one of the CRC-specific pathways. Within this pathway, NK cells solely communicated with myeloid cells under CRC conditions. HAVCR2+ NK cells from CRC patients suppressed NK cell-mediated cytotoxicity, indicating a reduction in immune surveillance. Overall, we elucidated the comprehensive UC and CRC immune microenvironments and NK cell-mediated immune responses. Our findings can aid in selecting therapeutic targets that increase the efficacy of immunotherapy.

Study facts

Organism
Homo sapiens
Platform
10x Genomics Chromium
Age group
adult
Disease groups
Anatomical sites

Data availability

  • Processed matrix

File types CSV

Files and samples

  • BT1303.counts.csv
  • BT1304.counts.csv
  • BT1305.counts.csv
  • BT1306.counts.csv
  • BT1307.counts.csv
  • E-MTAB-8107.idf.txt
  • E-MTAB-8107.sdrf.txt
  • sc5rJUQ026.counts.csv
  • sc5rJUQ033.counts.csv
  • sc5rJUQ039.counts.csv
  • sc5rJUQ042.counts.csv
  • sc5rJUQ043.counts.csv
  • sc5rJUQ045.counts.csv
  • sc5rJUQ046.counts.csv
  • sc5rJUQ050.counts.csv
  • sc5rJUQ051.counts.csv
  • sc5rJUQ053.counts.csv
  • sc5rJUQ058.counts.csv
  • sc5rJUQ060.counts.csv
  • sc5rJUQ064.counts.csv
  • scrEXT001.counts.csv
  • scrEXT002.counts.csv
  • scrEXT003.counts.csv
  • scrEXT009.counts.csv
  • scrEXT010.counts.csv
  • scrEXT011.counts.csv
  • scrEXT012.counts.csv
  • scrEXT013.counts.csv
  • scrEXT014.counts.csv
  • scrEXT018.counts.csv
  • scrEXT019.counts.csv
  • scrEXT020.counts.csv
  • scrEXT021.counts.csv
  • scrEXT022.counts.csv
  • scrEXT023.counts.csv
  • scrEXT024.counts.csv
  • scrEXT025.counts.csv
  • scrEXT026.counts.csv
  • scrEXT027.counts.csv
  • scrEXT028.counts.csv
  • scrEXT029.counts.csv
  • scrJUQ059.counts.csv
  • scrJUQ068.counts.csv
  • scrJUQ070.counts.csv
  • scrJUQ072.counts.csv
  • scrSOL001.counts.csv
  • scrSOL003.counts.csv
  • scrSOL004.counts.csv
  • scrSOL006.counts.csv
  • scrSOL007.counts.csv

Strengths & limitations for reuse

Strengths

  • Processed matrices are advertised
  • Participant mapping is available

Limitations

  • Not documented: raw counts are advertised
  • Not documented: cell metadata are advertised
  • Not documented: participant counts are documented
Extraction evidence & provenance

Each extracted field is shown with the source excerpt and location used to resolve it.

Assay

FieldValueEvidence
assay.assay_type single-cell RNA sequencing
Single-cell RNA sequencing of ovarian, colorectal and breast cancer

Section Study, offset —

assay.library_chemistry 10x Genomics V2 barcoding chemistry
Samples were processed using kits pertaining to V2 barcoding chemistry of 10X Genomics.

Section Protocols, offset —

assay.platform 10x Genomics Chromium
Chromium Single Cell 3′ or 5' Library, Gel Bead & Multiplex Kit and Chip Kit (10X Genomics

Section Protocols, offset —

assay.reference_genome hg38
aligned to the human reference genome hg38

Section Materials and methods — Selection criteria for the public scRNA-seq dataset, offset —

assay.sequencing_type scrna_seq
RNA-seq of coding RNA from single cells

Section Study, offset —

Cohort

FieldValueEvidence
cohort.age_group adult
Characteristics[developmental stage] adult

Section , offset —

Data_Assets

FieldValueEvidence
data_assets.open_access True
"isOpenAccess": "Y"

Section , offset —

data_assets.processed_matrix True
Processed Data

Section Assays and Data — Processed Data, offset —

data_assets.raw_reads False
Raw data files have been removed upon submitter's request.

Section Study, offset —

Processing

FieldValueEvidence
processing.cell_type_annotation_method SingleR and canonical marker genes
classified as T cells, B cells, plasma cells, and myeloid cells depending on cell type probability calculated by package named ‘SingleR’ and mean expressions of canonical marker genes

Section Materials and methods — Integration and cell type identification, offset —

processing.normalization_method Seurat NormalizeData and SCTransform
We then normalized the combined data using SCTransform.

Section Materials and methods — Integration and cell type identification, offset —

processing.quality_control_reported True
cells with at least 400 UMIs, between 200 and 6000 genes and less than 25% of mtRNA were retained

Section Protocols, offset —

Specimens

FieldValueEvidence
specimens.number_of_samples 49
"Sample count", "value": "49"

Section Samples, offset —

specimens.participant_to_sample_mapping_available True inferred
Characteristics[individual]

Section , offset —

specimens.specimen_type mixed
Samples were collected during resection surgery or biopsy

Section Protocols, offset —