The single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells in Chinese holstein cattle.

OBJECTIVES: This study was performed in the frame of a more extensive study dedicated to the integrated analysis of the single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells (PBMCs) with a large-scale GWAS of 45 complex traits in Chinese Holstein cattle. Lipopolysaccharide (LPS) is a crucial mediator of chronic inflammation to modulate immune responses. PBMCs include primary T and B cells, natural killer (NK) cells, monocytes (Mono), and dendritic cells (DC). How LPS stimulates PBMCs at the single-cell level in dairy cattle remains largely unknown. DATA DESCRIPTION: We sequenced 30,756 estimated single cells and mapped 26,141 of them (96.05%) with approximately 60,075 mapped reads per cell after quality control for four whole-blood treatments (no, 2 h, 4 h, and 8 h LPS) by single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). Finally, 7,107 (no), 9,174 (2 h), 6,741 (4 h), and 3,119 (8 h) cells were generated with ~ 15,000 total genes in the whole population. Therefore, the single-cell transcriptome and chromatin accessibility datasets in this study enable a further understanding of the cell types and functions of PBMCs and their responses to LPS stimulation in vitro.

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows.

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm-1 and 3017~2823 cm-1/1805~1734 cm-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide.

BACKGROUND: Gram-negative bacteria are important pathogens in cattle, causing severe infectious diseases, including mastitis. Lipopolysaccharides (LPS) are components of the outer membrane of Gram-negative bacteria and crucial mediators of chronic inflammation in cattle. LPS modulations of bovine immune responses have been studied before. However, the single-cell transcriptomic and chromatin accessibility analyses of bovine peripheral blood mononuclear cells (PBMCs) and their responses to LPS stimulation were never reported. RESULTS: We performed single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) in bovine PBMCs before and after LPS treatment and demonstrated that seven major cell types, which included CD4 T cells, CD8 T cells, and B cells, monocytes, natural killer cells, innate lymphoid cells, and dendritic cells. Bioinformatic analyses indicated that LPS could increase PBMC cell cycle progression, cellular differentiation, and chromatin accessibility. Gene analyses further showed significant changes in differential expression, transcription factor binding site, gene ontology, and regulatory interactions during the PBMC responses to LPS. Consistent with the findings of previous studies, LPS induced activation of monocytes and dendritic cells, likely through their upregulated TLR4 receptor. NF-κB was observed to be activated by LPS and an increased transcription of an array of pro-inflammatory cytokines, in agreement that NF-κB is an LPS-responsive regulator of innate immune responses. In addition, by integrating LPS-induced differentially expressed genes (DEGs) with large-scale GWAS of 45 complex traits in Holstein, we detected trait-relevant cell types. We found that selected DEGs were significantly associated with immune-relevant health, milk production, and body conformation traits. CONCLUSION: This study provided the first scRNAseq and scATAC-seq data for cattle PBMCs and their responses to the LPS stimulation to the best of our knowledge. These results should also serve as valuable resources for the future study of the bovine immune system and open the door for discoveries about immune cell roles in complex traits like mastitis at single-cell resolution.