Deep single-cell transcriptomic profiling of bovine milk somatic cells revealed expression of stem cell related transcription factors

Dolinar, Mateja (author), Dovč, Peter (author), Zorc, Minja (author)

Background/Objectives: Milk somatic cells reflect the cellular composition and functional state of the lactating mammary gland and represent a valuable, non-invasive source for transcriptomic studies. Single-cell RNA sequencing (scRNA-seq) enables cell-type-resolved analysis of bovine milk; however, sequencing depth strongly influences the detection of lowly expressed genes and the resolution of transcriptional cell states. The aim of this study was to further characterise the single-cell transcriptome of bovine milk somatic cells, with particular emphasis on high-resolution gene expression profiling and cellular heterogeneity. Methods: Milk somatic cells were isolated from two healthy Holstein Friesian cows in mid-lactation and profiled using a droplet-based scRNA-seq platform. Newly generated high-depth datasets were integrated with two previously published bovine milk scRNA-seq datasets using an identical bioinformatics pipeline. Data integration, clustering and cell-type annotation were performed using the Seurat framework, and transcription factor expression was evaluated across datasets with different sequencing depths. Results: Single-cell transcriptomic analysis revealed a diverse cellular landscape in bovine milk, comprising epithelial, progenitor, and immune cell populations. Unsupervised clustering identified 21 transcriptionally distinct clusters, including multiple CD8+ T-cell subpopulations, monocytes, neutrophils, mast cells, and B cells, as well as luminal epithelial and luminal progenitor cells. While overall cell-type composition was comparable across datasets, deeply sequenced samples exhibited higher transcriptomic complexity and enabled refined resolution of immune and epithelial subpopulations. Deeper sequencing facilitated the detection of low-abundance transcription factors that were not observed in lower-depth datasets. Among these, NANOG was detected exclusively in deeply sequenced samples, suggesting the presence of rare transcriptional states associated with cellular plasticity. Conclusions: This study expands the single-cell transcriptomic landscape of bovine milk somatic cells and demonstrates the importance of sequencing depth for resolving functional cellular heterogeneity. The results highlight milk as a powerful, non-invasive source for investigating mammary gland biology and cellular plasticity during lactation.

• Language: English
• Keywords: single-cell RNA sequencing, bovine milk, milk somatic cells, mammary gland, lactation, transcriptome, cattleHolstein Friesian cows
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Publication date: 24.03.2026
• Year: 2026
• Number of pages: 13 str.
• Numbering: Vol. 17, issue 4, [article no.] 365
• PID: 20.500.12556/RUL-181084
• UDC: 575:636.257
• ISSN on article: 2073-4425
• DOI: 10.3390/genes17040365
• COBISS.SI-ID: 272865539
• Publication date in RUL: 24.03.2026

Record is a part of a journal

• Title: Genes
• Shortened title: Genes
• Publisher: MDPI
• ISSN: 2073-4425
• COBISS.SI-ID: 523100185

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: govedo, mleko, mlečna žleza, somatske celice, genetika, matične celice, transkriptomika

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P4-0220
• Name: Primerjalna genomika in genomska biodiverziteta

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: J4-3095
• Name: Aplikacija sekvenciranja posameznih celic in strojnega učenja v biologiji mlečne žleze