Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

Cross-species enhancer prediction using machine learning.

Cis-regulatory elements (CREs) are non-coding parts of the genome that play a critical role in gene expression regulation. Enhancers, as an important example of CREs, interact with genes to influence complex traits like disease, heat tolerance and growth rate. Much of what is known about enhancers come from studies of humans and a few model organisms like mouse, with little known about other mammalian species. Previous studies have attempted to identify enhancers in less studied mammals using comparative genomics but with limited success. Recently, Machine Learning (ML) techniques have shown promising results to predict enhancer regions. Here, we investigated the ability of ML methods to identify enhancers in three non-model mammalian species (cattle, pig and dog) using human and mouse enhancer data from VISTA and publicly available ChIP-seq. We tested nine models, using four different representations of the DNA sequences in cross-species prediction using both the VISTA dataset and species-specific ChIP-seq data. We identified between 809,399 and 877,278 enhancer-like regions (ELRs) in the study species (11.6-13.7% of each genome). These predictions were close to the ~8% proportion of ELRs that covered the human genome. We propose that our ML methods have predictive ability for identifying enhancers in non-model mammalian species. We have provided a list of high confidence enhancers at https://github.com/DaviesCentreInformatics/Cross-species-enhancer-prediction and believe these enhancers will be of great use to the community.

Adaptive selection signatures in river buffalo with emphasis on immune and major histocompatibility complex genes.

River buffalo is an agriculturally important species with many traits, such as disease tolerance, which promote its use worldwide. Highly contiguous genome assemblies of the river buffalo, goat, pig, human and two cattle subspecies were aligned to study gene gains and losses and signs of positive selection. The gene families that have changed significantly in river buffalo since divergence from cattle play important roles in protein degradation, the olfactory receptor system, detoxification and the immune system. We used the branch site model in PAML to analyse single-copy orthologs to identify positively selected genes that may be involved in skin differentiation, mammary development and bone formation in the river buffalo branch. The high contiguity of the genomes enabled evaluation of differences among species in the major histocompatibility complex. We identified a Babesia-like L1 LINE insertion in the DRB1-like gene in the river buffalo and discuss the implication of this finding.

MicroRNA breed and parent-of-origin effects provide insights into biological pathways differentiating cattle subspecies in fetal liver.

Introduction: MicroRNAs (miRNAs) play a crucial role in regulating gene expression during key developmental processes, including fetal development. Brahman (Bos taurus indicus) and Angus (Bos taurus taurus) cattle breeds represent two major cattle subspecies with strikingly different phenotypes. Methods: We analyzed miRNA expression in liver samples of purebred and reciprocal crosses of Angus and Brahman to investigate breed and parent-of-origin effects at the onset of accelerated fetal growth. Results: We identified eight novel miRNAs in fetal liver samples and 14 differentially expressed miRNAs (DEMs) between purebred samples. Correlation of gene expression modules and miRNAs by breed and parent-of-origin effects revealed an enrichment of genes associated with breed-specific differences in traits such as heat tolerance (Brahman) and fat deposition (Angus). We demonstrate that genes predicted to be targets of DEMs were more likely to be differentially expressed than non-targets (p-value < 0.05). We identified several miRNAs (bta-miR-187, bta-miR-216b, bta-miR-2284c, bta-miR-2285c, bta-miR-2285cp, bta-miR-2419-3p, bta-miR-2419-5p, and bta-miR-11984) that showed similar correlation patterns as bta-miR-2355-3p, which has been associated with the glutamatergic synapse pathway, a key facilitator of heat tolerance. Furthermore, we report Angus-breed-specific miRNAs (bta-miR-2313-5p, btamiR-490, bta-miR-2316, and bta-miR-11990) that may be involved in fat deposition. Finally, we showed that the DEMs identified in fetal liver are involved in Rap1, MAPK, and Ras signalling pathways, which are important for fetal development, muscle development and metabolic traits such as fat metabolism. Conclusion: Our work sheds light on the miRNA expression patterns that contribute to gene expression differences driving phenotypic differences in indicine and taurine cattle.