Biology subjects

McCalla, S. G.

Publications and source records attributed to McCalla, S. G..

6 recordsLinked to original sources

bioRxiv2025

Physical, chemical, and structural properties of human gastric organoid-derived mucus

The gastric mucus layer protects the epithelium from gastric acid and ingested pathogens. However, studies of human gastric mucus have been limited due to poor accessibility of native human mucus and the abundance of contaminants in these samples. Here, we explored the potential of human gastric organoids as models for mucus production. Immunofluorescence staining confirmed that the organoids produced mucus containing MUC5AC and MUC6. The luminal mucus had viscoelastic properties similar to those of native human gastric mucus, as determined by particle tracking microrheology. To collect organoid-produced gastric mucus, termed bioengineered gastric mucus (BGM), organoids were cultured as monolayers at the air-liquid interface (ALI), and apically-secreted mucus was harvested and analyzed by MUC5AC ELISA, proteomics, CryoFE-SEM, and bulk rheometry. BGM contained high-molecular weight molecules also found in native gastric mucus, including MUC5AC. Proteomic analysis confirmed that BGM contained MUC5AC, MUC6, MUC1, and other stomach-specific molecules such as gastricsin, olfactomedin 4, and gastrokine. CryoFE-SEM showed that both BGM and native mucus had a porous structure and a characteristic honeycomb scaffold. Bulk rheometry confirmed that BGM exhibited shear thinning and predominantly elastic behavior, consistent with native mucus. Collectively, these findings indicate that BGM is an accessible alternative to native gastric mucus that can be produced on-demand for in vitro studies. New and NoteworthyWe demonstrate the structural and functional similarities of organoid-derived gastric mucus and native mucus collected from human patients. The bioengineered gastric mucus mimics its native counterpart in its proteomic profile, physical architecture, and viscoelasticity. This work highlights the translational potential of organoid-derived mucus for functional investigations of the human gastric mucus layer.

cell biology↗

bioRxiv2025

Uncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation

Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurately estimate TFA and downstream GRNs. We apply our method to simulated and real expression data in yeast and mammalian systems and show improved quality of inferred GRNs for both bulk and single-cell datasets. Regularized TFA offers benefits to a variety of other GRN inference algorithms, including those that have traditionally been used with expression alone, in both bulk and scRNA-seq settings. We used the inferred GRN to prioritize key regulators for the mouse Embryonic Stem Cell (mESC) state and validate 58 regulators experimentally. We identified both known and novel regulators of the mESC state and further validate the targets of 4 known and novel regulators. Our validation experiments suggest that computationally inferred networks can capture functional targets of TFs with higher precision than estimated in current benchmarks, however, it is important to generate context-specific gold standards.

bioinformatics↗

bioRxiv2024

Transcriptomic analysis reveals vector attraction to potato virus Y is mediated through temporal regulation of TERPENE SYNTHASE 1 (TPS1)

Virus-plant dynamics change over time, influencing interactions between plants and insect vectors. However, the signaling pathways and regulators that control these temporal responses remain largely unknown. In this study, we used insect performance and preference bioassays, RNA-Seq, and genetic tools to identify underlying mechanisms mediating temporal variation in plant-virus-vector interactions. We show that settlement and fecundity of the aphid vector, Myzus persicae, is increased on potato virus Y (PVY)-infected Nicotiana benthamiana plants two weeks after inoculation but not after six weeks. RNA-Seg analysis revealed transcripts related to plant defense and amino acid biosynthesis are upregulated in response to PVY infection and down regulated in response to aphid herbivory, and these patterns changed over time. Based on this analysis we identified a sesquiterpene synthase gene, terpene synthase 1 (NbTPS1), that is upregulated early in PVY infection, but not at later infection time points. Using virus-induced gene silencing and transient overexpression in N. benthamiana we demonstrate that PVY induction of NbTPS1 is required for increased aphid attraction to PVY-infected plants in the early stages of infection. Taken together, PVY temporally regulates transcriptional pathways related to plant defense responses and volatile organic compounds that influence aphid vector performance and preference.

plant biology↗

bioRxiv2021

Modular Derivation and Unbiased Single-cell Analysis of Regional Human Hindbrain And Spinal Neurons Enables Discovery of Nuanced Transcriptomic Patterns along Developmental Axes

Our inability to derive the vast neuronal diversity of the posterior central nervous system (pCNS) using human pluripotent stem cells (hPSCs) poses a major impediment to understanding human neurodevelopment and disease in the hindbrain and spinal cord. Here we establish a modular differentiation paradigm that recapitulates patterning along both the rostrocaudal (R/C) and dorsoventral (D/V) axes of the pCNS, enabling derivation of any neuronal phenotype with discrete regional specificity. First, neuromesodermal progenitors (NMPs) with discrete Hox profiles are efficiently converted to pCNS progenitors (pCNSPs). Then by tuning D/V signaling, pCNSPs are directed to ventral Shh-dependent MNs (MNs) and locomotor interneurons (INs) or dorsal TGF-{beta}-dependent proprioceptive INs and TGF-{beta}-independent sensory INs. We applied D/V protocols to NMPs spanning the R/C axis for expansive single-cell RNA-sequencing (scRNAseq) analysis. By implementing a novel computational pipeline comprising sparse non-negative matrix factorization, consensus clustering, and combinatorial gene expression pattern identification, we detect hundreds of transcriptional markers within region-specific neuronal phenotypes, enabling discovery of gene expression patterns along the developmental axes. These findings highlight the potential of these resources to advance a mechanistic understanding of pCNS development, expand the potential and accuracy of in vitro models, and inform novel regenerative therapeutic strategies.

neuroscience↗

bioRxiv2021

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on six published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found little to no benefit from imputation for network inference, which is further dataset-dependent. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

bioinformatics↗

bioRxiv2019

Defining reprogramming checkpoints from single-cell analysis of induced pluripotency

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a novel combination of epigenetic and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress towards iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play novel functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenetic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

developmental biology↗