Unraveling Population Heterogeneity using Single-Cell Analysis

The human body contains approximately 100 trillion cells, encompassing distinct cell types that serve diverse functions. Understanding cell population heterogeneity is vital for uncovering different biological functions and mechanisms. In addition, cells at transition during continual processes, such as development, reprogramming, and disease, are essential for painting the entire blueprint and highlighting critical stages of the progression trajectory. For instance, cell fate engineering holds much promise for generating clinically valuable cell types from mature somatic cells. Nonetheless, current reprogramming protocols are inefficient, and charting the changes in cell identity during such processes can help design strategies to mitigate the off-target and increase efficiency. RNA-sequencing allows us to study transcript abundance and dissect different genetic features. Prior to single-cell level sequencing, bulk-level transcriptomics have demonstrated power at a lower resolution to distinguish populations and identify differential gene markers. The advent of single-cell RNA-sequencing technologies has brought us a new era of exploring the small world inside individual cells via their transcriptome profiles. Single-cell RNA-sequencing takes a snapshot of individual cells, enabling the dissection of population composition and capture of cells at different states in complex biological systems. Cell type annotation has been a long-standing interest in understanding cell identities from gene profiles. Yet, manual annotations require prior knowledge of cell-type-specific gene signatures and are labor-intensive and time-consuming. Automated annotation approaches are in demand for exponentially growing single-cell datasets.In response to such demand, many computational approaches have been developed. However, they classify cells in a discrete, categorical manner, limiting their application in continuous biological systems. Focusing on continual processes, we designed a computational tool, \u27Capybara,\u27 to measure cell identity as a continuum at a single-cell resolution. This approach enables the classification of discrete cell identities and recognizes cells harboring hybrid identities, supporting a quantitative cell-fate transition metric. After benchmarking against other classifiers and validation with ground-truth lineage data, we apply Capybara to a diverse range of cellular programming and reprogramming protocols: The application to direct cardiac reprogramming uncovers a patterning bias and a hybrid state between atrial and ventricular cardiomyocytes; Capybara reveals previously uncharacterized patterning deficiencies in motor neuron programming, instructing a new approach to alleviate the lack of proper patterning; Further, we apply Capybara to our in-house system, direct reprogramming of fibroblast to induced endoderm progenitors, and find a putative in vivo correlate for this engineered cell type that has, to date, remained poorly defined. These findings highlight the utility of Capybara to dissect cell identity and fate transitions in development, reprogramming, and disease. Finally, we further explore the direct cardiac reprogramming system using the comprehensive set of tools developed in the lab. We resolve lineage relationships in this system using CellTagging, find key regulatory transcription factors using CellOracle, and evaluate small molecules\u27 effect on the patterning bias using Capybara. In summary, I have developed a tool to highlight cell fate transitions and reveal insight into cellular heterogeneity in different continuous biological processes. Further investigation in the transition states by integration with other data modalities and experimental approaches may help pinpoint key checkpoints for successful reprogramming, allowing future interventions to improve the efficiency and fidelity of cell fate engineering

Numerical study of the effects of gravity on soot formation in laminar coflow methane/air diffusion flames under different air stream velocities

Numerical simulations of laminar coflow methane/air diffusion flames at atmospheric pressure and different gravity levels were conducted to gain a better understanding of the effects of gravity on soot formation by using relatively detailed gas-phase chemistry and complex thermal and transport properties coupled with a semi-empirical twoequation soot model. Thermal radiation was calculated using the discrete-ordinates method coupled with a non-grey model for the radiative properties of CO, CO\u2082, H\u2082O, and soot. Calculations were conducted for three coflow air velocities of 77.6, 30, and 5 cm/s to investigate how the coflowing air velocity affects the flame structure and soot formation at different levels of gravity. The coflow air velocity has a rather significant effect on the streamwise velocity and the fluid parcel residence time, especially at reduced gravity levels. The flame height and the visible flame height in general increase with decreasing the gravity level. The peak flame temperature decreases with decreasing either the coflow air stream velocity or the gravity level. The peak soot volume fraction of the flame at microgravity can either be greater or less than that of its normal gravity counterpart, depending on the coflowair velocity.At sufficiently high coflowair velocity, the peak soot volume fraction increases with decreasing the gravity level. When the coflow air velocity is low enough, soot formation is greatly suppressed at microgravity and extinguishment occurs in the upper portion of the flame with soot emission from the tip of the flame owing to incomplete oxidation. The numerical results provide further insights into the intimate coupling between flame size, residence time, thermal radiation, and soot formation at reduced gravity level. The importance of thermal radiation heat transfer and coflow air velocity to the flame structure and soot formation at microgravity is demonstrated for the first time.NRC publication: Ye

Dosage compensation on the active X chromosome minimizes transcriptional noise of X-linked genes in mammals

Comparison of gene expression variation in autosomal and X-linked genes reveals that high transcriptional noise is not a necessary consequence of haploid expression

Gene regulatory network reconfiguration in direct lineage reprogramming

In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming

CellTag Indexing: Genetic barcode-based sample multiplexing for single-cell genomics

High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies

Single-cell analysis reveals regional reprogramming during adaptation to massive small bowel resection in mice

BACKGROUND & AIMS: The small intestine (SI) displays regionality in nutrient and immunological function. Following SI tissue loss (as occurs in short gut syndrome, or SGS), remaining SI must compensate, or adapt ; the capacity of SI epithelium to reprogram its regional identity has not been described. Here, we apply single-cell resolution analyses to characterize molecular changes underpinning adaptation to SGS. METHODS: Single-cell RNA sequencing was performed on epithelial cells isolated from distal SI of mice following 50% proximal small bowel resection (SBR) vs sham surgery. Single-cell profiles were clustered based on transcriptional similarity, reconstructing differentiation events from intestinal stem cells (ISCs) through to mature enterocytes. An unsupervised computational approach to score cell identity was used to quantify changes in regional (proximal vs distal) SI identity, validated using immunofluorescence, immunohistochemistry, qPCR, western blotting, and RNA-FISH. RESULTS: Uniform Manifold Approximation and Projection-based clustering and visualization revealed differentiation trajectories from ISCs to mature enterocytes in sham and SBR. Cell identity scoring demonstrated segregation of enterocytes by regional SI identity: SBR enterocytes assumed more mature proximal identities. This was associated with significant upregulation of lipid metabolism and oxidative stress gene expression, which was validated via orthogonal analyses. Observed upstream transcriptional changes suggest retinoid metabolism and proximal transcription factor Creb3l3 drive proximalization of cell identity in response to SBR. CONCLUSIONS: Adaptation to proximal SBR involves regional reprogramming of ileal enterocytes toward a proximal identity. Interventions bolstering the endogenous reprogramming capacity of SI enterocytes-conceivably by engaging the retinoid metabolism pathway-merit further investigation, as they may increase enteral feeding tolerance, and obviate intestinal failure, in SGS

Synchronous post-acceleration of laser-driven protons in helical coil targets by controlling the current dispersion

Post-acceleration of protons in helical coil targets driven by intense, ultrashort laser pulses can enhance ion energy by utilizing the transient current from the targets’ self-discharge. The acceleration length of protons can exceed a few millimeters, and the acceleration gradient is of the order of GeV/m. How to ensure the synchronization between the accelerating electric field and the protons is a crucial problem for efficient post-acceleration. In this paper, we study how the electric field mismatch induced by current dispersion affects the synchronous acceleration of protons. We propose a scheme using a two-stage helical coil to control the current dispersion. With optimized parameters, the energy gain of protons is increased by four times. Proton energy is expected to reach 45 MeV using a hundreds-of-terawatts laser, or more than 100 MeV using a petawatt laser, by controlling the current dispersion