Biology subjects

Shaulsky, G.

Publications and source records attributed to Shaulsky, G..

3 recordsLinked to original sources

bioRxiv2026

Single-cell foundation models transfer across deep evolutionary distances

Single-cell foundation models are trained mostly on human and other vertebrate cells, yet are proposed as general-purpose models. Whether they transfer far beyond their training species is unknown. We evaluated zero-shot embeddings from Geneformer, scGPT and UCE against PCA and scVI in developmental atlases of Dictyostelium discoideum and Caenorhabditis elegans, separated from humans by 1.2 billion and 600 million years. Without retraining, the models recovered developmental order and preserved organism-specific cell identities. UCE encodes genes by protein sequence and could use 97% of measured genes, whereas Geneformer and scGPT, after ortholog mapping, used under one-fifth. UCE performed most consistently, often exceeding species-native baselines. Restricted to ortholog-mappable genes, it still scored above vocabulary-based models on five of six annotation tasks, some narrowly. Foundation models can thus yield informative cell embeddings across more than a billion years of evolution, and protein-sequence gene representations make these embeddings more complete and often more accurate.

bioinformatics↗

bioRxiv2026

dictyExpress: an integrated browser for bulk and single-cell Dictyostelium transcriptomics

Summary dictyExpress 3.0 is a React/TypeScript reimplementation of the Dictyostelium discoideum transcriptomics web server, first introduced in 2009, that unifies bulk and single-cell exploration. A long-standing gene-expression resource for the Dictyostelium community, dictyExpress is also accessible through dictyBase, the central organism database for Dictyostelium. The new release preserves the curated bulk RNA-seq dashboards of earlier versions while adding an interactive module for a developmental scRNA-seq time course of wild-type AX4 and two cAMP-signalling mutants. Following visual analytics principles, interactive visualizations are coordinated through brushing and linking, with bulk time courses, differential expression, gene-ontology enrichment and the UMAP workspace sharing a common gene selection, enabling bookmarkable and exportable cross-modal analysis in one interface for Dictyostelium transcriptomics. Availability and implementation dictyExpress is available at https://app.dictyexpress.org/. Source code: https://github.com/biolab/dictyexpress-js. Contact lena.trnovec@fri-uni-lj.si, blaz.zupan@fri.uni-lj.si

bioinformatics↗

bioRxiv2021

Transcriptional milestones in Dictyostelium development

Development of the social amoeba Dictyostelium discoideum begins by starvation of single cells and ends in multicellular fruiting bodies 24 hours later. These major morphological changes are accompanied by sweeping gene expression changes, encompassing nearly half of the 13,000 genes in the genome. To explore the relationships between the transcriptome and developmental morphogenesis, we performed time-series RNA-sequencing analysis of the wild type and 20 mutant strains with altered morphogenesis. These strains exhibit arrest at different developmental stages, accelerated development, or terminal morphologies that are not typically seen in the wild type. Considering eight major morphological transitions, we identified 1,371 milestone genes whose expression changes sharply between two consecutive transitions. We also identified 1,099 genes as members of 21 regulons, which are groups of genes that remain coordinately regulated despite the genetic, temporal, and developmental perturbations in the dataset. The gene annotations in these milestones and regulons validate known transitions and reveal several new physiological and functional transitions during development. For example, we found that DNA replication genes are co-regulated with cell division genes, so they are co-expressed in mid-development even though chromosomal DNA is not replicated at that time. Altogether, the dataset includes 486 transcriptional profiles, across developmental and genetic conditions, that can be used to identify new relationships between gene expression and developmental processes and to improve gene annotations. We demonstrate the utility of this resource by showing that the cycles of aggregation and disaggregation observed in allorecognition-defective mutants involve a dedifferentiation process. We also show unexpected variability and sensitivity to genetic background and developmental conditions in two commonly used genes, act6 and act15, and robustness of the coaA gene. Finally, we propose that gpdA should be used as a standard for mRNA quantitation because it is less sensitive to genetic background and developmental conditions than commonly used standards. The dataset is available for democratized exploration without the need for programming skills through the web application dictyExpress and the data mining environment Orange.

genomics↗