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

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

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.

• Jezik: Angleški jezik
• Ključne besede: single-cell RNA sequencing, bovine milk, milk somatic cells, mammary gland, lactation, transcriptome, cattleHolstein Friesian cows
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: BF - Biotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Datum objave: 24.03.2026
• Leto izida: 2026
• Št. strani: 13 str.
• Številčenje: Vol. 17, issue 4, [article no.] 365
• PID: 20.500.12556/RUL-181084
• UDK: 575:636.257
• ISSN pri članku: 2073-4425
• DOI: 10.3390/genes17040365
• COBISS.SI-ID: 272865539
• Datum objave v RUL: 24.03.2026

Gradivo je del revije

• Naslov: Genes
• Skrajšan naslov: Genes
• Založnik: Multidisciplinary Digital Publishing Institute (MDPI)
• ISSN: 2073-4425
• COBISS.SI-ID: 523100185

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: govedo, mleko, mlečna žleza, somatske celice, genetika, matične celice, transkriptomika

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P4-0220
• Naslov: Primerjalna genomika in genomska biodiverziteta

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: J4-3095
• Naslov: Aplikacija sekvenciranja posameznih celic in strojnega učenja v biologiji mlečne žleze