Asian single-cell genomics data with age-related metadata

The Chan Zuckerberg Initiative (CZI) supports several projects involving Asian single-cell genomics data with age-related metadata through its CELLxGENE Discover platform and affiliated research networks. Here are key resources:

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## **Asian Immune Diversity Atlas (AIDA)**  

**Focus**: Immune cell variation across six Asian populations (Chinese, Japanese, Korean, Indian, Malay, Thai)[1][4].  

**Methods**:  

- Single-cell transcriptomics (5’ scRNA-seq)  

- TCR/BCR profiling  

- Enhancer RNA analysis  

**Cohort**:  

- Over 2,000 healthy individuals spanning 20 ancestrally diverse Asian groups  

- Includes non-urban and underrepresented populations  

- Analyzes effects of **age**, ancestry, sex, and environmental factors on immune phenotypes[4].  

**Access**: Data will contribute to the Human Cell Atlas and likely integrate with CZ CELLxGENE Discover[1][4].  

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## **CZ CELLxGENE Discover Platform**  

**Key Features**:  

- Hosts >85 million single cells from 1,317 datasets  

- Includes metadata filters for **age**, cell type, and tissue[5][6].  

- Tools for analysis, visualization, and AI model training  

**Relevant Data**:  

- Search using filters like `organism: Homo sapiens`, `tissue: blood`, and `ethnicity: Asian`  

- Age metadata available for many datasets (e.g., developmental stages, adult vs. aging)[5][6][9].  

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## **Billion Cells Project**  

**Partners**: 10x Genomics, Ultima Genomics  

**Scope**:  

- Includes primary human cell models, potentially covering Asian cohorts  

- Focuses on genetic perturbations across cell types and tissues[2][7].  

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## **How to Access**  

1. Visit [CZ CELLxGENE Discover](https://chanzuckerberg.github.io/cellxgene-census/)  

2. Use the Census API or PyTorch loaders for large-scale analysis[6][9].  

3. Filter datasets using variables like `donor_age` and `ethnicity`.  

For AIDA-specific data, monitor updates via the [Human Cell Atlas-Asia network](https://chanzuckerberg.com/science/programs-resources/cell-science/ancestry-networks/)[1][4].

Citations:

[1] https://chanzuckerberg.com/science/programs-resources/cell-science/seednetworks/asian-immune-diversity-atlas/

[2] https://www.insideprecisionmedicine.com/topics/precision-medicine/chan-zuckerberg-initiative-10x-genomics-and-ultima-genomics-launch-billion-cells-project/

[3] https://chanzuckerberg.com/science/programs-resources/cell-science/seednetworks/

[4] https://chanzuckerberg.com/science/programs-resources/cell-science/ancestry-networks/immune-cell-atlas-of-asian-populations/

[5] https://www.nature.com/articles/d41586-024-01217-y

[6] https://chanzuckerberg.github.io/cellxgene-census/

[7] https://www.prnewswire.com/news-releases/chan-zuckerberg-initiative-launches-billion-cells-project-with-10x-genomics-and-ultima-genomics-to-advance-ai-in-biology-302369647.html

[8] https://www.genomeweb.com/genetic-research/chan-zuckerberg-initiative-launches-new-york-biohub-immune-cell-based-early

[9] https://www.biorxiv.org/content/10.1101/2023.10.30.563174v1.full.pdf

[10] https://www.science.org/doi/10.1126/science.abf1970

[11] https://www.science.org/doi/10.1126/science.abf3041

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Answer from Perplexity: pplx.ai/share

 

https://pmc.ncbi.nlm.nih.gov/articles/PMC9871912/

. 2023 Jan 10;13:1081889. doi: 10.3389/fimmu.2022.1081889

Single-cell RNA sequencing reveals the molecular features of peripheral blood immune cells in children, adults and centenarians

Jinjie Zhong ^1,2,3, Rong Ding ⁴, Huimin Jiang ^1,2,3, LongFei Li ⁴, Junli Wan ^1,2, Xiaoqian Feng ^1,2,3, Miaomiao Chen ^1,2, Liping Peng ^1,2,3, Xiaoqin Li ^1,2, Jing Lin ^1,2, Haiping Yang ^1,2,3, Mo Wang ^1,2,3, Qiu Li ^1,2,3,*, Qilin Chen ^1,2,3,*

PMCID: PMC9871912  PMID: 36703979

Age groups in single-cell genomics studies are often categorized based on developmental and aging stages. Here are examples from relevant datasets:

### **Age Groups in Single-Cell Genomics Studies**

1. **Multi-Age PBMC Profile (PMC9871912)**

   - **Children (Group 1, G1)**: 1–12 years old.

   - **Young Adults (Group 2, G2)**: 20–30 years old.

   - **Middle-Aged Adults (Group 3, G3)**: 30–60 years old.

   - **Aged Adults (Group 4, G4)**: 60–80 years old.

   - **Supercentenarians (Group 5, G5)**: >110 years old[4].

2. **Transcriptomic Age Profiling Study**

   - Age groups were used to predict the chronological age of cells. Differences were observed between adolescents, adults, and aged adults across various cell types[1].

3. **PanSci Dataset**

   - Includes cellular data across five life stages in mice, showing age-specific shifts in cell populations across organs. This approach might be mirrored in human studies for similar age group classifications[6].

These age groups provide a framework for analyzing cellular changes and aging processes across different life stages.

Citations:

[1] https://www.nature.com/articles/s42003-024-07094-5

[2] https://virtualcellmodels.cziscience.com/dataset/0192d920-34c0-70a6-971c-5c24943f5a13

[3] https://www.genengnews.com/topics/omics/the-100-million-cell-challenge-announces-winning-projects/

[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC9871912/

[5] https://cellxgene.cziscience.com

[6] https://www.rockefeller.edu/news/36993-study-reveals-how-unexpected-shifts-in-cell-populations-are-revising-our-understanding-of-the-aging-process/

[7] https://pubmed.ncbi.nlm.nih.gov/40112801/

[8] https://www.biorxiv.org/content/10.1101/2023.10.30.563174v1.full.pdf

[9] https://www.ega-archive.org/studies/phs003848

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