Highlights d Human pancreatic islets are key drivers of diabetes and related pathophysiology d TIGER integrates omics and expression regulatory variation in 514 human islet samples d TIGER expression regulatory variation allows the identification of diabetes effector genes d The integrated human islet data in TIGER are publicly available through http://tiger.bsc.es

Background Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification. Results We propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets. Conclusion On average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.

Over the last decade, increasing attention has been paid to the molecular adaptations used by organisms to cope with thermal stress. However, to date, few studies have focused on thermophilic species living in hot, arid climates. In this study, we explored molecular adaptations to heat stress in the thermophilic ant genus Cataglyphis, one of the world's most thermotolerant animal taxa. We compared heat tolerance and gene expression patterns across six Cataglyphis species from distinct phylogenetic groups that live in different habitats and experience different thermal regimes. We found that all six species had high heat tolerance levels with critical thermal maxima (CT max ) ranging from 43℃ to 45℃ and a median lethal temperature (LT50) ranging from 44.5℃ to 46.8℃. Transcriptome analyses revealed that, although the number of differentially expressed genes varied widely for the six species (from 54 to 1118), many were also shared. Functional annotation of the differentially expressed and co-expressed genes showed that the biological pathways involved in heat-shock responses were similar among species and were associated with four major processes: the regulation of transcriptional machinery and DNA metabolism; the preservation of proteome stability; the elimination of toxic residues; and the maintenance of cellular integrity. Overall, our results suggest that molecular responses to heat stress have been evolutionarily conserved in the ant genus Cataglyphis and that their diversity may help workers withstand temperatures close to their physiological limits.

In the gastrulating mouse embryo, epiblast cells delaminate at the primitive streak to form mesoderm and definitive endoderm, through an epithelial-mesenchymal transition.Mosaic expression of a membrane reporter in nascent mesoderm enabled recording cell shape and trajectory through live imaging. Upon leaving the streak, cells changed shape and extended protrusions of distinct size and abundance depending on the neighboring germ layer, as well as the region of the embryo. Embryonic trajectories were meandrous but directional, while extra-embryonic mesoderm cells showed little net displacement.Embryonic and extra-embryonic mesoderm transcriptomes highlighted distinct guidance, cytoskeleton, adhesion, and extracellular matrix signatures. Specifically, intermediate filaments were highly expressed in extra-embryonic mesoderm, while live imaging for F-actin showed abundance of actin filaments in embryonic mesoderm only. Accordingly, RhoA or Rac1 conditional deletion in mesoderm inhibited embryonic, but not extra-embryonic mesoderm migration.Overall, this indicates separate cytoskeleton regulation coordinating the morphology and migration of mesoderm subpopulations.