---
title: "Speciesformer: a cross-species generative foundation model for virtual cell-state modeling"
id: "biorxiv-13-speciesformer-learns-conserved-cellular-states-for-cross-species-generative"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-13-speciesformer-learns-conserved-cellular-states-for-cross-species-generative"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1?rss=1"
published_at: "2026-09-23T09:50:14.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Speciesformer: a cross-species generative foundation model for virtual cell-state modeling
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-13-speciesformer-learns-conserved-cellular-states-for-cross-species-generative
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1?rss=1)
- **Published At:** 2026-09-23T09:50:14.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors introduce **Speciesformer**, a cross-species generative single-cell foundation model designed to learn conserved cellular states and model state transitions across biological contexts. - Speciesformer is pretrained on a large compendium called **SpeciesCorpus**, which contains 131 million single-cell profiles spanning 11 species, 154 tissues, and over 923 cell types. - The model maps species-specific genes into a shared, evolution-informed gene space, enabling comparison and transfer of information across species. - Its encoder produces transferable representations of cells and genes that support biological representation probing and cross-species knowledge transfer, creating a common state space to distinguish conserved programs from context-dependent variation. - Built on the shared representation, Speciesformer implements a unified **generative** architecture to model cellular states and transitions under both semantic and interventional conditions. - Capabilities reported include bidirectional generation between **transcriptomic** states and biological text descriptions, and prediction of post-perturbation transcriptomes from initial cell states plus intervention descriptions. - The model is claimed to generalize to predict outcomes in previously unobserved cellular contexts, by unifying evolutionary variation, biological semantics and conditional state transitions. - The work is presented as a preprint; it has not been peer reviewed. The authors declare no competing interests. - Specific implementation details, quantitative performance metrics, and evaluation results were not reported in the provided source abstract and would require consultation of the full manuscript or supplementary materials for verification.
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Jiacheng Wang 1 King Abdullah University of Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jiacheng%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJiacheng%2BWang%2B) * [ORCID record for Jiacheng Wang](http://orcid.org/0000-0001-8580-0493 "Open in new tab") Jiaqi Dong 1 King Abdullah University of Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jiaqi%2BDong%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Dong%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJiaqi%2BDong%2B) Guowei Li 2 Independent Researcher; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Guowei%2BLi%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Li%20G&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGuowei%2BLi%2B) Chao Fang 3 LC-Bio Technologies (Hangzhou) CO., LTD * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Chao%2BFang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Fang%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AChao%2BFang%2B) Liwei Liu 2 Independent Researcher; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Liwei%2BLiu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Liu%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALiwei%2BLiu%2B) Xin Gao 1 King Abdullah University of Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Xin%2BGao%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Gao%20X&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AXin%2BGao%2B) * [ORCID record for Xin Gao](http://orcid.org/0000-0002-7108-3574 "Open in new tab") * For correspondence: xin.gao@kaust.edu.sa * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5802717/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5802717/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5802717/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5802717/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.22.752128v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5802717/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Cells across species are governed by evolutionarily conserved biological programs, yet their molecular states and responses are reshaped by species, tissue and cellular context. A central challenge for virtual cell modeling is therefore to learn cellular states and state transitions that separate transferable biological principles from context-specific variation. Existing single-cell foundation models have advanced cellular representation learning, but most remain focused on single-species analysis, discriminative tasks or specialized forms of generation. Here we present Speciesformer, a cross-species generative single-cell foundation model that integrates evolutionary representation learning with virtual cell-state generation. Speciesformer is pretrained on SpeciesCorpus, comprising 131 million cells from 11 species, 154 tissues, and more than 923 cell types, and maps species-specific genes into a shared evolution-informed gene space. Its encoder learns transferable cell and gene representations that support biological representation probing and cross-species knowledge transfer, providing a common state space for resolving conserved and context-dependent cellular programs. Building on this shared representation, Speciesformer uses a unified generative architecture to model cellular states and state transitions under semantic and interventional conditions, enabling bidirectional generation between transcriptomic states and biological text descriptions, as well as prediction of post-perturbation transcriptomes from initial cell states and intervention descriptions, including in previously unobserved cellular contexts. By unifying evolutionary variation, biological semantics and conditional state transitions, Speciesformer extends cross-species foundation modeling toward a generative virtual cell framework for representing, describing and predicting cellular states across biological contexts. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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