The production of milk by dairy cows far exceeds the nutritional needs of the calf and is vital for the economical use of dairy cattle. High milk yield is a unique production trait that can be effectively enhanced through traditional selection methods. The process of lactation in cows serves as an excellent model for studying the biological aspects of lactation with the aim of exploring the mechanistic base of this complex trait at the cellular level. In this study, we analyzed the milk transcriptome at the single-cell level by conducting scRNA-seq analysis on milk samples from two Holstein Friesian cows at mid-lactation (75 and 93 days) using the 10× Chromium platform. Cells were pelleted and fat was removed from milk by centrifugation. The cell suspension from each cow was loaded on separate channels, resulting in the recovery of 9313 and 14,544 cells. Library samples were loaded onto two lanes of the NovaSeq 6000 (Illumina) instrument. After filtering at the cell and gene levels, a total of 7988 and 13,973 cells remained, respectively. We were able to reconstruct different cell types (milk-producing cells, progenitor cells, macrophages, monocytes, dendritic cells, T cells, B cells, mast cells, and neutrophils) in bovine milk. Our findings provide a valuable resource for identifying regulatory elements associated with various functions of the mammary gland such as lactation, tissue renewal, native immunity, protein and fat synthesis, and hormonal response.

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.