Datasets:

longevity-db
/

terekhova2023

dataset_name: tereshkova2023_pbmc_aging
annotations_creators:
  - expert-generated
language:
  - en
multilinguality: 'no'
pretty_name: Single-cell Atlas of Human Blood Aging (Terekhova et al. 2023)
task_categories:
  - cell-type-classification
size_categories:
  - 1M<n<10M
license: cc-by-4.0
dataset_type: biomedical

Dataset Card for `tereshkova2023_processed`

Dataset Summary

This dataset is a single-cell multi-omic atlas of peripheral blood mononuclear cells (PBMCs) from 166 healthy donors aged 25–85, comprising ~2 million cells profiled with scRNA-seq, TCR/BCR-seq, and surface protein barcoding (CITE-seq). It supports cell type annotation, immune aging trajectory modeling, and multimodal integration. It is described in:

Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB–CD8+ memory T cells and accumulation of type 2 memory T cells
— Terekhova et al., Immunity, Volume 56, Issue 12 (2023)

Transformation Summary

The raw data was processed with the following steps:

File Acquisition: Downloaded .tar.gz archives from Synapse (all_pbmcs.tar.gz, raw_counts_h5ad.tar.gz) and moved them into a structured raw/terekhova2022 directory.
Extraction: Extracted pbmc_gex_raw_with_var_obs.h5ad from the tarball.
Demultiplexing Assignments: Downloaded and merged cell-level assignments from multiple *_assignment_cell_patient.csv files into a single table, then joined this with the .obs table in the AnnData object.
Metadata Integration: Manually downloaded mmc2.xlsx, reshaped it to long format per tube/visit, and mapped donor metadata (age, sex, ethnicity, BMI, visit) to cells using the patient ID.
Annotation Cleanup: Renamed columns for clarity (nCount_RNA → raw_sum, Cluster_names → unique_cell_type, etc.), dropped unused donor ID.
Final Output: Saved the processed AnnData object as processed/terekhova2022_processed.h5ad.

Supported Tasks and Benchmarks

Immune Cell Type Annotation: 55 annotated PBMC subtypes spanning all major lineages.
Aging Trajectory Modeling: Suitable for lifespan analyses (ages 25–85).
VDJ Repertoire Analysis: Includes matched TCR/BCR sequences.
Multimodal Learning: Combines surface proteins and gene expression for CITE-seq tasks.
Rare Subtype Discovery: Detects rare populations, such as NKG2C+GZMB–CD8+ memory T cells.

Languages

Textual metadata (e.g. donor demographics) are in English.

Dataset Structure

Data Instances

Each instance corresponds to a single cell, with:

Gene expression (UMIs)
Surface protein (CITE-seq)
VDJ sequences (where available)
Annotated metadata (age, sex, visit, ethnicity, etc.)

Data Splits

train: Age groups 25–64
test: Age 65+

Dataset Creation

Curation Rationale

Designed to enable high-resolution studies of immune aging using a large, high-quality cohort of healthy adults, overcoming prior limitations in sample size and longitudinal tracking.

Source Data

IRB-approved PBMC collection at Washington University in St. Louis.
Subjects: healthy, fasting, non-obese, free of chronic inflammatory diseases.
Assays: 10x 5′v2 with TotalSeq-C and VDJ enrichment.

Preprocessing Details

Demultiplexing: HTO (Seurat) + genotype-based (Souporcell)
Filtering: Gene count, mitochondrial percentage, doublets
Batch correction: Harmony
Clustering and annotation: Seurat + Scanpy
Metadata merge from Excel and CSV sources, structured by tube ID

Licensing Information

This dataset is distributed under the Creative Commons BY 4.0 license. Please cite the original paper in any publications or derived work.

Citation

@article{terekhova2023pbmc,
  title={Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB–CD8+ memory T cells and accumulation of type 2 memory T cells},
  author={Terekhova, Marina and Swain, Amanda and Bohacova, Pavla and others},
  journal={Immunity},
  volume={56},
  number={12},
  pages={2836--2854.e9},
  year={2023},
  publisher={Cell Press}
}

Dataset Card for tereshkova2023_processed