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].  

---

## **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].  

---

## **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].  

---

## **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

---

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

---

Answer from Perplexity: pplx.ai/share

 https://www.cdc.gov/prams/php/questionnaires/index.html
Pregnancy Risk Pregnancy Risk Assessment Monitoring System (PRAMS)

single-cell RNA sequencing (scRNA-seq) and single-nuclei RNA sequencing (snRNA-seq)

 Both single-cell RNA sequencing (scRNA-seq) and single-nuclei RNA sequencing (snRNA-seq) are techniques used to profile gene expression at high resolution, but they differ in the biological material they analyze and in some aspects of their workflows and applications.

Single-Cell RNA Sequencing (scRNA-seq)

• What It Is:
  scRNA-seq involves isolating entire individual cells from a tissue sample. Once isolated, the mRNA from each cell is reverse-transcribed into cDNA, amplified, and sequenced.

• Key Advantages:

  • Cellular Heterogeneity: It reveals the transcriptomic differences between cells, allowing the identification of distinct cell types, rare subpopulations, and dynamic cellular states.

  • Broad RNA Capture: Because it analyzes whole cells, both cytoplasmic and nuclear RNAs are profiled, providing a more complete picture of gene expression.

• Common Applications:

  • Mapping cellular diversity in complex tissues

  • Investigating developmental processes

  • Studying disease mechanisms at a single-cell resolution

Single-Nuclei RNA Sequencing (snRNA-seq)

• What It Is:
  snRNA-seq focuses on isolating nuclei rather than whole cells. This approach is especially useful for tissues where obtaining intact cells is challenging—such as in frozen samples, brain tissue, or fibrous tissues.

• Key Advantages:

  • Tissue Accessibility: Nuclei can be isolated from archived or frozen specimens where cell membranes may be compromised.

  • Less Dissociation Bias: The process minimizes the stress and technical artifacts that can occur during cell dissociation, which might otherwise alter gene expression profiles.

• Limitations:

  • Transcript Coverage: Since only nuclear RNA is captured, the method might miss some cytoplasmic mRNAs, and typically, fewer transcripts are detected per nucleus.

• Common Applications:

  • Studying tissues that are difficult to dissociate into intact cells

  • Analyzing archived or frozen samples where traditional scRNA-seq is not feasible

Comparison and Choosing the Right Method

• Material & Sample Type:

  • Use scRNA-seq when fresh tissue is available and intact cells can be easily isolated, providing comprehensive transcriptomic data.

  • Use snRNA-seq for challenging tissues (e.g., brain, fibrous tissues) or archived samples where cell integrity is compromised.

• Data Output:

  • scRNA-seq often results in a richer dataset including both nuclear and cytoplasmic RNAs.

  • snRNA-seq typically offers a snapshot biased toward nuclear RNA, which may include pre-mRNA and transcripts in the process of being processed.

• Technical Considerations:

  • scRNA-seq may require more delicate handling during cell dissociation, which can sometimes introduce artifacts.

  • snRNA-seq simplifies the process by working with nuclei, often reducing the impact of dissociation-induced gene expression changes.

In summary, both methods are powerful tools for dissecting cellular complexity. The choice between them depends largely on the nature of the tissue, the condition of the sample, and the specific research questions being addressed.

snRNAseq 465 donors Rush

 https://www.synapse.org/Synapse:syn31512863

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Items	6
Storage Location	s3://ad-knowledge-portal-main/data

Sample processing: Dorsolateral Prefrontal Cortex (DLFPC) tissue specimens from 465 unique donors were received frozen from the Rush Alzheimer’s Disease Center. We observed variability in the morphology of these tissue specimens with differing amounts of gray and white matter and presence of attached meninges. Working on ice throughout, we carefully dissected to remove white matter and meninges, when present. The following steps were also conducted on ice: about 50-100mg of gray matter tissue was transferred into the dounce homogenizer (Sigma Cat No: D8938) with 2mL of NP40 Lysis Buffer [0.1% NP40, 10mM Tris, 146mM NaCl, 1mM CaCl2, 21mM MgCl2, 40U/mL of RNAse inhibitor (Takara: 2313B)]. Tissue was gently dounced while on ice 25 times with Pestle A followed by 25 times with Pestle B, then transferred to a 15mL conical tube. 3mL of PBS + 0.01% BSA (NEB B9000S) and 40U/mL of RNAse inhibitor were added for a final volume of 5mL and then immediately centrifuged with a swing bucket rotor at 500g for 5 mins at 4°C. Samples were processed 2 at a time, the supernatant was removed, and the pellets were set on ice to rest while processing the remaining tissues to complete a batch of 8 samples. The nuclei pellets were then resuspended in 500ml of PBS + 0.01% BSA and 40U/mL of RNAse inhibitor. Nuclei were filtered through 20um pre-separation filters (Miltenyi: 130-101-812) and counted using the Nexcelom Cellometer Vision and a 2.5ug/ul DAPI stain at 1:1 dilution with cellometer cell counting chamber (Nexcelom CHT4-SD100-002).

Library preparation and sequencing: 5,000 nuclei from each of 8 participants were then pooled into one sample, and the 40,000 nuclei in around 15-30ul volume were run on the 10X Single Cell RNA-Seq Platform using the Chromium Single Cell 3’ Reagent Kits version 3. Libraries were made following the manufacturer’s protocol, briefly, single nuclei were partitioned into nanoliter scale Gel Bead-In-EMulsion (GEMs) in the Chromium controller instrument where cDNA share a common 10X barcode from the bead. Amplified cDNA is measured by Qubit HS DNA assay (Thermo Fisher Scientific: Q32851) and quality assessed by BioAnalyzer (Agilent: 5067-4626). This WTA (whole transcriptome amplified) material was diluted to <8ng/ml and processed through v3 library construction, and resulting libraries were quantified again by Qubit and BioAnalzyer. Libraries from 4 channels were pooled and sequenced on 1 lane of Illumina HiSeqX by The Broad Institute’s Genomics Platform, for a target coverage of around 1 million reads per channel.

Demultiplexing of snRNAseq reads: Because our snRNAseq library consisted of nuclei from eight individuals, original individuals of each droplet were inferred by harnessing SNPs in snRNAseq reads. We employed two different procedures, depending on whether all eight individuals had been genotyped with WGS. When eight individuals were genotyped, we used demuxlet software. From the WGS-based VCF file of 1,196 ROS/MAP individuals, we extracted SNPs that were in transcribed regions, passed a filter of GATK, and at least one of the eight individuals had its alternate allele. The extracted SNP genotype data were fed to demuxlet along with BAM file generated by CellRanger. When fewer than eight individuals were genotyped, we used freemuxlet, which clusters droplets based on SNPs in snRNAseq reads and generates a VCF file of snRNAseq-based genotypes of the clusters. The number of clusters was specified to be eight. The snRNAseq-based VCF file was filtered for genotype quality > 30 and compared with available WGS genotypes using the bcftools gtcheck command. Each WGS-genotyped individual was assigned to one of droplet clusters by visually inspecting a heatmap of the number of discordant SNP sites between snRNAseq and WGS. The above two procedures converged to a table that mapped droplet barcodes onto inferred individuals.

The resulting demultiplexed cell barcodes mapped to ROSMAP individualIDs can be found in the mapping file at syn34572333. These are preliminary mapping results provided here by the study authors before publication. If revisions are made, the file will be updated and changes will be noted in subsequent AD Portal data releases.

Processing of single-nucleus RNAseq reads: For each batch of snRNAseq FASTQ files, the CellRanger software (v6.0.0; 10x Genomics) was used to map reads onto the reference human genome GRCh38, to collapse reads by UMI, and to count UMI per gene per droplet. As a transcriptome model, the “GRCh38-2020-A” file set distributed by 10X Genomics was used. The “--include-introns” option was set to incorporate reads mapped to intronic region of nuclear pre-mRNA into UMI counts. To call cells among the entire droplets, the “remove-background” module of CellBender (https://github.com/broadinstitute/CellBender) was applied to raw UMI count matrices. The admixture of ambient RNA was estimated and subtracted from UMI counts by CellBender.

Single-nucleus RNA-seq analysis: We analyzed each of the snRNAseq libraries (syn51123521) for cell-type annotation, low quality removal of cells and doublet detection. We then followed with cell-type specific QCs, sub-clustering analysis and functional annotations. The final cell-type objects are found in syn53366818. For more information on the data curation and annotation process see the Methods section. The full source code is available on GitHub.

Once completed, our atlas holds ~1.64 million nuclei from 465 participants, spanning 95 cell types/subpopulations.

• The full list of nuclei annotations is available under cell-annotation.full.atlas.csv (syn55219673). While we analyzed tissue samples of 465 participants, we were not able to reassign nuclei for all, resulting in a mapping to 450 participants. Nuclei which were not reassigned to a participant have an individualID value of NA (n=23,964).

After we finished defining the cell atlas we followed with associating subpopulations with participants’ disease traits. For that we only used participants (and their nuclei) that passed QCs of sufficient numbers of nuclei (see methods). This reduced the number of participants used (for post-atlas analysis) to 437.

• The list of nuclei belonging to the 437 participants used for downstream analysis is available under cell-annotations.n437.csv (syn53694215)
• Between our original preprint (March 2023) and the final version of the atlas we were able to recover the mapping of 13 additional participants (their nuclei were already part of the atlas but were missing the mapping to a participant). For reproducibility of studies using the original preprint version of the atlas we keep the annotation file cell-annotation.n424.csv (syn52363764)

Wiki created on06/22/2022 10:16 AMand last modified on04/12/2024 1:31 PM

	Name		Size	Modified On	ID	Download

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Cell type objects 04/08/2024 12:43 PM

cell-annotation.full-atlas.csv 04/09/2024 11:57 AM  +

cell-annotation.n424.csv 04/09/2024 11:58 AM  +

cell-annotation.n437.csv 04/09/2024 11:58 AM  +

count matrices 04/08/2024 12:42 PM

raw 02/23/2023 12:45 PM

1. Folder created by @abby.vanderlinden on 6/1/2022 5:31 PM
2. /
3. Folder last modified by @abby.vanderlinden on 9/2/2022 4:59 PM

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Experimental Mode

Dorsolateral Prefrontal Cortex (DLFPC) tissue specimens from 465 unique donors were received frozen from the Rush Alzheimer’s Disease Center. We observed variability in the morphology of these tissue specimens with differing amounts of gray and white matter and presence of attached meninges. Working on ice throughout, we carefully dissected to remove white matter and meninges, when present. The following steps were also conducted on ice: about 50-100mg of gray matter tissue was transferred into the dounce homogenizer (Sigma Cat No: D8938) with 2mL of NP40 Lysis Buffer [0.1% NP40, 10mM Tris, 146mM NaCl, 1mM CaCl2, 21mM MgCl2, 40U/mL of RNAse inhibitor (Takara: 2313B)]. Tissue was gently dounced while on ice 25 times with Pestle A followed by 25 times with Pestle B, then transferred to a 15mL conical tube. 3mL of PBS + 0.01% BSA (NEB B9000S) and 40U/mL of RNAse inhibitor were added for a final volume of 5mL and then immediately centrifuged with a swing bucket rotor at 500g for 5 mins at 4°C. Samples were processed 2 at a time, the supernatant was removed, and the pellets were set on ice to rest while processing the remaining tissues to complete a batch of 8 samples. The nuclei pellets were then resuspended in 500ml of PBS + 0.01% BSA and 40U/mL of RNAse inhibitor. Nuclei were filtered through 20um pre-separation filters (Miltenyi: 130-101-812) and counted using the Nexcelom Cellometer Vision and a 2.5ug/ul DAPI stain at 1:1 dilution with cellometer cell counting chamber (Nexcelom CHT4-SD100-002).

Library preparation and sequencing: 5,000 nuclei from each of 8 participants were then pooled into one sample, and the 40,000 nuclei in around 15-30ul volume were run on the 10X Single Cell RNA-Seq Platform using the Chromium Single Cell 3’ Reagent Kits version 3. Libraries were made following the manufacturer’s protocol, briefly, single nuclei were partitioned into nanoliter scale Gel Bead-In-EMulsion (GEMs) in the Chromium controller instrument where cDNA share a common 10X barcode from the bead. Amplified cDNA is measured by Qubit HS DNA assay (Thermo Fisher Scientific: Q32851) and quality assessed by BioAnalyzer (Agilent: 5067-4626). This WTA (whole transcriptome amplified) material was diluted to <8ng/ml and processed through v3 library construction, and resulting libraries were quantified again by Qubit and BioAnalzyer. Libraries from 4 channels were pooled and sequenced on 1 lane of Illumina HiSeqX by The Broad Institute’s Genomics Platform, for a target coverage of around 1 million reads per channel

bioRxiv preprint doi: https://doi.org/10.1101/2023.03.07.531493 ;

 this version posted March 9, 2023. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Cellular dynamics across aged human brains uncover a multicellular cascade leading to Alzheimer’s disease Gilad Sahar Green1, Masashi Fujita2*, Hyun-Sik Yang3,4*, Mariko Taga2, Cristin McCabe5, Anael Cain1, Charles C. White4, Anna K. Schmidtner1, Lu Zeng2, Yangling Wang6, Aviv Regev5,7, Vilas Menon2+, David A. Bennett6+, Naomi Habib1+&, Philip L. De Jager2,4+&