TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments

SinCMat: A single-cell-based method for predicting functional maturation transcription factors

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM.

A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion

Cellular conversion can be induced by perturbing a handful of key transcription factors (TFs). Replacement of direct manipulation of key TFs with chemical compounds offers a less laborious and safer strategy to drive cellular conversion for regenerative medicine. Nevertheless, identifying optimal chemical compounds currently requires large-scale screening of chemical libraries, which is resource intensive. Existing computational methods aim at predicting cell conversion TFs, but there are no methods for identifying chemical compounds targeting these TFs. Here, we develop a single cell-based platform (SiPer) to systematically prioritize chemical compounds targeting desired TFs to guide cellular conversions. SiPer integrates a large compendium of chemical perturbations on non-cancer cells with a network model and predicted known and novel chemical compounds in diverse cell conversion examples. Importantly, we applied SiPer to develop a highly efficient protocol for human hepatic maturation. Overall, SiPer provides a valuable resource to efficiently identify chemical compounds for cell conversion.

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation. We construct probabilistic Boolean networks (PBNs) from single-cell RNA sequencing data of two different cell states to model hematopoietic transcription factors cross-talk. This was achieved by a “sampled network” approach, which enabled us to construct large networks. The interventions to induce transdifferentiation consisted of permanently activating or deactivating each of the TFs and determining the probability mass transfer of steady-state probabilities from the departure to the destination cell type or state. Our findings support the common assumption that TFs that are differentially expressed between the two cell types are the best intervention points to achieve transdifferentiation. TFs whose interventions are found to transdifferentiate progenitor B cells into monocytes include EBF1 down-regulation, CEBPB up-regulation, TCF3 down-regulation, and STAT3 up-regulation.

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Differentially expressed transcription factors are the best for transdifferentiation

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Probabilistic Boolean networks (PBNs) are used to model transdifferentiation using the scRNAseq data at one time point

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A new approach works for a large number of network nodes

Narrative engineering of the liver

Liver organoids are primary or pluripotent stem cell-derived three-dimensional structures that recapitulate regenerative or ontogenetic processes in vitro towards biomedical applications including disease modelling and diagnostics, drug safety and efficacy prediction, and therapeutic use. The cellular composition and structural organization of liver organoids may vary depending on the goal at hand, and the key challenge in general is to direct their development in a rational and controlled fashion for gaining targeted maturity, reproducibility, and scalability. Such endeavor begins with a detailed understanding of the biological processes in space and time behind hepatogenesis, followed by precise translation of these narrative processes through a bioengineering approach. Here, we discuss advancements in liver organoid technology through the lens of 'narrative engineering' in an attempt to synergize evolving understanding around molecular and cellular landscape governing hepatogenesis with engineering-inspired approaches for organoidgenesis.

High throughput deep proteomic analysis of seminal plasma from stallions with contrasting semen quality

Seminal plasma proteins and pathways associated with sperm motility have not been elucidated in stallions. Therefore, in the current study, using the high throughput LC/MS-MS approach, we profiled stallion seminal plasma proteins and identified the proteins and pathways associated with sperm motility. Seminal plasma from six stallions producing semen with contrasting sperm motility (n = 3 each high-and low-motile group) was utilized for proteomic analysis. We identified a total of 1687 proteins in stallion seminal plasma, of which 1627 and 1496 proteins were expressed in high- (HM) and low- motile (LM) sperm of stallions, respectively. A total number of 1436 proteins were co-expressed in both the groups; 191 (11%) and 60 (3.5%) proteins were exclusively detected in HM and LM groups, respectively. A total of 220 proteins were upregulated (>1-fold change) and 386 proteins were downregulated in SP from LM group stallions as compared to HM group stallions, while 830 proteins were neutrally expressed in both the groups. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed dysregulation of the important proteins related to mitochondrial function, acrosome, and sperm cytoskeleton in the seminal plasma of stallions producing ejaculates with low sperm motility. High abundance of peroxiredoxins and low abundance of seminal Chaperonin Containing TCP1 Complex (CCT) complex and Annexins indicate dysregulated oxidative metabolism, which might be the underlying etiology for poor sperm motility in LM group stallions. In conclusion, the current study identified the seminal plasma proteomic alterations associated with poor sperm motility in stallions; the results indicate that poor sperm motility in stallions could be associated with altered expression of seminal plasma proteins involved in oxidative metabolism.

NEUBOrg: Artificially Induced Pluripotent Stem Cell-Derived Brain Organoid to Model and Study Genetics of Alzheimer's Disease Progression

Alzheimer's disease (AD) is the most common type of neurodegenerative diseases. There are over 44 million people living with the disease worldwide. While there are currently no effective treatments for AD, induced pluripotent stem cell-derived brain organoids have the potential to provide a better understanding of Alzheimer's pathogenesis. Nevertheless, developing brain organoid models is expensive, time consuming and often does not reflect disease progression. Using accurate and inexpensive computer simulations of human brain organoids can overcome the current limitations. Induced whole brain organoids (aiWBO) will greatly expand our ability to model AD and can guide wet lab research. In this study, we have successfully developed and validated artificially induced a whole brain organoid platform (NEUBOrg) using our previously validated machine learning platform, DeepNEU (v6.1). Using NEUBorg platform, we have generated aiWBO simulations of AD and provided a novel approach to test genetic risk factors associated with AD progression and pathogenesis.

TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments

Generation of desired cell types by cell conversion remains a challenge. In particular, derivation of novel cell subtypes identified by single‐cell technologies will open up new strategies for cell therapies. The recent increase in the generation of single‐cell RNA‐sequencing (scRNA‐seq) data and the concomitant increase in the interest expressed by researchers in generating a wide range of functional cells prompted us to develop a computational tool for tackling this challenge. Here we introduce a web application, TransSynW, which uses scRNA‐seq data for predicting cell conversion transcription factors (TFs) for user‐specified cell populations. TransSynW prioritizes pioneer factors among predicted conversion TFs to facilitate chromatin opening often required for cell conversion. In addition, it predicts marker genes for assessing the performance of cell conversion experiments. Furthermore, TransSynW does not require users' knowledge of computer programming and computational resources. We applied TransSynW to different levels of cell conversion specificity, which recapitulated known conversion TFs at each level. We foresee that TransSynW will be a valuable tool for guiding experimentalists to design novel protocols for cell conversion in stem cell research and regenerative medicine.