Biology subjects

Boyer, L. A.

Publications and source records attributed to Boyer, L. A..

3 recordsLinked to original sources

bioRxiv2025

Self-amplifying RNA enables rapid, durable, integration-free programming of hiPSCs

Genetic modification of human induced pluripotent stem cells (hiPSCs) is a powerful approach to measure and manipulate the cellular processes underlying differentiation and disease. Conventional genetic engineering of hiPSC lines requires a laborious process involving transfection, selection and expansion that can result in karyotypic abnormalities or transgene silencing during differentiation, limiting their applications. Self-amplifying RNA (saRNA) delivery is a potential alternative integration-free method for durable expression of transgenes. Here, we used saRNA to deliver transcription factors and functional reporters in hiPSCs and demonstrate that expression can persist for weeks. Specifically, saRNA delivery enables highly efficient forward programming to Ngn2-induced neurons and enables measurement of functional reporters over time. We show that a single transfection of saRNA encoded jRCaMP1b reporter in hiPSCs generates sustained expression throughout differentiation to 3D cardiac spheroids. The persistence of the reporter allows measurement of calcium dynamics at a single-cell and population level over weeks, allowing tracking of cardiomyocyte maturation and drug responses. Together, our systematic analysis shows that saRNA provides sustained transgene expression in hiPSCs, supporting integration- free cell-fate programming and measurement of functional reporters in clinically relevant model systems. HighlightsO_LIA single saRNA transfection generates durable transgene expression C_LIO_LIsaRNA transfection of Ngn2 in hiPSCs results in robust neuronal differentiation C_LIO_LIsaRNA-delivery of functional reporters enables single-cell analysis of primary and hiPSC-derived cells C_LIO_LIsaRNA-based sensor allows monitoring of maturation and drug responses in 3D cardiac spheroids C_LI

bioengineering↗

bioRxiv2025

Assessment of dispersion metrics for estimating single-cell transcriptional variability

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is scale-invariant. In contrast, the Gini index displayed paradoxical behavior, and Shannon entropy was not scale-invariant. Thus, we next applied the variance-to-mean ratio to measure transcriptional variability in a publicly available single-cell dataset of embryonic hearts from a mouse model of maternal hyperglycemia. Our data show that many genes display transcriptional variability within the same cell type, and that this variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age. Notably, many of the genes and pathways with highest transcriptional variability were not identified as differentially expressed, and have in fact been implicated in maternal hyperglycemia in other studies, suggesting that transcriptional variability can provide additional biologically relevant information beyond what is observed from studying mean expression alone.

genomics↗

bioRxiv2023

HiExM: high-throughput expansion microscopy enables scalable super-resolution imaging

Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells; however, current methods limit the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables [~]4.2x expansion of cells within individual wells, across multiple wells, and between plates. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms to greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, on human cardiomyocytes (CMs) as measured by Hoechst signal across the nucleus. We show a dose dependent effect on nuclear DNA that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications. Significance StatementExpansion microscopy (ExM) is an accessible and widely used technique for super-resolution imaging of fixed biological specimens. For many ExM users, slide-based sample preparation and manual imaging limit the number of experimental conditions and samples that can be processed in parallel. Here, we develop a simple and inexpensive device that enables ExM within the wells of a standard 96-well cell culture plate. We show that samples prepared with our workflow can be imaged with a high-throughput autonomous confocal microscope, allowing for scalable super-resolution image acquisition, greatly increasing data output. Our device retains the accessibility of ExM while extending its application to research questions that require the analysis of many conditions, treatments, and time points.

bioengineering↗