Generation of marmoset primordial germ cell-like cells under chemically defined conditions

Primordial germ cells (PGCs) are the embryonic precursors of sperm and oocytes, which transmit genetic/epigenetic information across generations. Mouse PGC and subsequent gamete development can be fully reconstituted in vitro, opening up new avenues for germ cell studies in biomedical research. However, PGCs show molecular differences between rodents and humans. Therefore, to establish an in vitro system that is closely related to humans, we studied PGC development in vivo and in vitro in the common marmoset monkey Callithrix jacchus (cj). Gonadal cjPGCs at embryonic day 74 express SOX17, AP2Ɣ, BLIMP1, NANOG, and OCT4A, which is reminiscent of human PGCs. We established transgene-free induced pluripotent stem cell (cjiPSC) lines from foetal and postnatal fibroblasts. These cjiPSCs, cultured in defined and feeder-free conditions, can be differentiated into precursors of mesendoderm and subsequently into cjPGC-like cells (cjPGCLCs) with a transcriptome similar to human PGCs/PGCLCs. Our results not only pave the way for studying PGC development in a non-human primate in vitro under experimentally controlled conditions, but also provide the opportunity to derive functional marmoset gametes in future studies.

Rapid and synchronous chemical induction of replicative-like senescence via a small molecule inhibitor

Cellular senescence is acknowledged as a key contributor to organismal ageing and late-life disease. Though popular, the study of senescence in vitro can be complicated by the prolonged and asynchronous timing of cells committing to it and by its paracrine effects. To address these issues, we repurposed a small molecule inhibitor, inflachromene (ICM), to induce senescence to human primary cells. Within 6 days of treatment with ICM, senescence hallmarks, including the nuclear eviction of HMGB1 and -B2, are uniformly induced across IMR90 cell populations. By generating and comparing various high throughput datasets from ICM-induced and replicative senescence, we uncovered a high similarity of the two states. Notably though, ICM suppresses the pro-inflammatory secretome associated with senescence, thus alleviating most paracrine effects. In summary, ICM rapidly and synchronously induces a senescent-like phenotype thereby allowing the study of its core regulatory program without confounding heterogeneity.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Bayesian Markov models improve the prediction of binding motifs beyond first order

Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.