Human Reference (Homo sapiens)

Cellular agriculture engineers livestock cells, but the largest and best-annotated single-cell corpora — and the most mature genome-scale metabolic reconstructions — are human. This page collects those human artifacts, because they are the practical substrate cell-ag modeling builds on: human single-cell foundation-model corpora are the pretraining base that cross-species transfer methods adapt to livestock, and the human GEMs are the homology templates and biomass-function donors from which species-specific livestock reconstructions inherit their reaction networks. Human is not a cultivated-meat target species — it is the reference substrate.

Single-cell & perturbation corpora

Genecorpus-30M

Genecorpus-30M is the pretraining corpus for Geneformer — a Hugging Face Datasets collection of ~30 million human single-cell transcriptomes assembled from publicly available scRNA-seq studies covering a broad range of human tissues and cell states. Each cell is encoded as a rank-ordered gene-expression “sentence” suitable for masked-language-model pretraining; the corpus is distributed under standard HF Datasets tooling with versioned snapshots. For cellular agriculture, Genecorpus-30M is the de-facto starting substrate for any human-cell foundation-model training run — and the template that cross-species fine-tuning approaches like SATURN (Papers.md ref #118) and UCE (ref #119) build on to transfer to livestock species where annotated single-cell data is sparse. Companion to Papers.md ref #111 (Theodoris et al. 2023, Nature).

Perturb-Sapiens

Perturb-Sapiens is Arc Institute’s Hugging Face Datasets release of large-scale perturbational single-cell measurements aggregated from public Perturb-seq, CROP-seq, and ECCITE-seq experiments, plus internal Arc data, covering 70+ human cell lines. The dataset is the training substrate for Arc’s State virtual cell model (Papers.md ref #57) and is a companion data product within the Arc Virtual Cell Atlas alongside Tahoe-100M and scBaseCount. For cellular agriculture, Perturb-Sapiens is the most comprehensive public source of cellular-perturbation-response data available — the methodology template for any future livestock-cell-perturbation atlas, and a useful benchmark for evaluating cross-species transfer of perturbation-response models.

Arc Virtual Cell Atlas

The Arc Virtual Cell Atlas is Arc Institute’s open-data initiative providing the substrate datasets for virtual-cell-modeling research, hosted on GitHub with documentation and uniformly processed releases, plus mirroring on Google Cloud’s BigQuery Public Data marketplace for cloud-native analytics. The Atlas aggregates several large-scale single-cell datasets under a single curation umbrella:

Tahoe-100M

A 100-million-cell drug-perturbation dataset profiling cancer-cell-line responses to >1,100 small molecules across 50+ cancer cell lines via large-scale Perturb-seq. The largest publicly available drug-perturbation single-cell dataset at time of release; methodology directly transferable to any future cell-ag work involving small-molecule modulators of differentiation, proliferation, or media response.

scBaseCount

An AI-agent-curated, uniformly processed, and autonomously updated single-cell data repository aggregating thousands of public scRNA-seq studies into a single harmonized reference — the data-engineering substrate that an autonomously updating virtual-cell atlas requires. Companion to Papers.md ref #126 (Youngblut et al. 2025).

Parse Biosciences 10M PBMC Atlas

A publicly released ~10-million peripheral blood mononuclear cell (PBMC) single-cell RNA-seq experiment from Parse Biosciences, demonstrating the throughput of their Evercode WT Mega platform. Includes harmonized cell-type annotations and represents the largest single-experiment PBMC atlas available at time of release. For cellular agriculture, useful both as a benchmark dataset for evaluating single-cell-FM batch-effect handling and scaling at extreme throughput, and as immune-cell reference data for cultivated-meat applications involving immune-cell co-cultures or contamination screening.

Genome-scale metabolic models

GEMs are SBML-formatted reconstructions of an organism’s metabolic network — every reaction, every metabolite, every gene-protein-reaction mapping — and are the input data structure for the constraint-based modeling tools listed in Software.md / Metabolic Modeling & Strain Design. The human reconstructions below are the upstream reference from which the cell-ag livestock GEMs (catalogued on the per-species pages) inherit network structure and curation conventions.

Recon3D / Human1 / HMR — Homo sapiens (template / upstream reference)

The human genome-scale metabolic reconstructions — Recon3D (Brunk et al. 2018, Nature Biotechnology), Human-GEM / Human1 (Robinson et al. 2020, Science Signaling), and the underlying HMR2 — are the foundational human GEMs from which most mammalian-cell models (including the cell-ag GEMs in the per-species pages of this directory) inherit reaction networks, biomass equations, and curation conventions. Direct use in cell-ag is rare; they’re more often used as homology templates or biomass-function donors for species-specific reconstructions. Recon3D source is at github.com/SBRG/Recon3D with the structural-systems-biology companion library at github.com/SBRG/ssbio.

References: Papers.md #86 (Brunk et al. 2018, Nature Biotechnology) for Recon3D; Papers.md #87 (Robinson et al. 2020, Science Signaling) for Human-GEM.

Curation source: These entries are long-standing CAAIL curation, migrated from the prior flat Datasets.md. They are cross-species reference substrate rather than cultivated-meat-species data, so — unlike the per-species pages — they are not drawn from the Todhunter et al. 2024 supplemental.