Biology subjects

Herman, A. C.

Publications and source records attributed to Herman, A. C..

3 recordsLinked to original sources

bioRxiv2026

Clinical grade cryopreservation unlocks transplant ready human pancreatic and stem cell derived islets for diabetes therapy

Pancreatic islet transplantation can restore endogenous insulin production and offers a potential cure for diabetes, but its clinical impact has been limited by the inability to preserve large numbers of functional islets for timely use. Cryopreservation could provide an on-demand supply, yet conventional methods cause ice formation, cell injury, and loss of insulin secretion. We developed CryoMesh, a vitrification platform that combines a low-toxicity cryoprotectant with a thermally conductive, biocompatible mesh to enable ultra-rapid cooling and rewarming for ice-free cryopreservation. This approach supports long-term, clinical-scale preservation of both human pancreatic and stem cell-derived islets while maintaining viability, architecture, mitochondrial integrity, and glucose-responsive insulin secretion. Human islets preserved for up to one year restored normoglycemia in diabetic mice, with complete recovery of function and no increase in immunogenicity or loss of potency. The combination of high viability, recovery, and functional preservation at clinical scale has not been achieved previously. By decoupling islet isolation and manufacture from transplantation, CryoMesh enables extended quality, potency, and safety testing, cost-effective batch production, and global banking and distribution. These capabilities remove a major barrier to curative cell therapy for diabetes and establish a generalizable strategy for preserving complex multicellular therapeutics. One Sentence SummaryA clinical scale vitrification platform enables long-term banking of human pancreatic and stem cell-derived islets for transplantation.

cell biology↗

bioRxiv2025

Spatial Transcriptomics Reveals that the Local Immune Response to Placental Guinea Pig

Cytomegalovirus infection can disrupt placental development and function either by directly infecting placental cells or by eliciting a pathogenic immune response. The relative contributions of these two mechanisms to adverse pregnancy outcomes remains poorly understood. In this study, we used spatial transcriptomics to quantify host and viral gene expression at the maternal-fetal interface at near single-cell resolution. Guinea pig cytomegalovirus (GPCMV) infection after mid-gestation causes focal infections at the base of the main placenta. Samples for spatial transcriptomics were collected from guinea pigs infected with GPCMV at 35 days gestation. Viral loads and the location of infected cells in placentas were assessed using virus-specific droplet digital PCR and in situ hybridization at 21 days post-infection. Representative placentas were sectioned onto Visium Spatial Gene Expression Slides and sequencing libraries were prepared from six infected and six uninfected tissue sections. Spatial transcriptomes from 33,687 55-{micro}m spots were generated and used in subsequent analyses. To assess how infection affected gene expression at the maternal-fetal interface, a combination of graph-based clustering and manual classification was used to assign spatial transcriptomes to clusters representative of different anatomic regions. Infection dysregulated more transcripts in the decidua and junctional zone than in the labyrinth or non-capillarized syncytium. Notably, infection downregulated transcripts involved in lipid metabolism and upregulated transcripts involved in antiviral defense and chemokine signaling. A second analysis compared the spatial transcriptomes of GPCMV-infected cells and their immediate microenvironment with similar regions in uninfected placentas. A transcriptional signature indicative of immune activation was clear in this comparison, and the local placental response to cytomegalovirus infection was driven by upregulated chemokine signaling. Thus, spatial transcriptomics revealed regional patterns of gene expression in the guinea pig placenta and illuminated how the host response to GPCMV may compromise placental function. Author SummaryThe placenta supports fetal development while also acting as an immune barrier against bloodborne pathogens. Cytomegalovirus (CMV), the most common viral cause of congenital infections and preventable neurologic disability in children, evades host defenses to infect the placenta. How CMV affects the placenta and fetal health remains poorly understood. Using a guinea pig model of CMV infection during pregnancy and spatial transcriptomics, a recently-developed method that enables gene expression to be studied at near-cellular resolution, this study compared normal and infected placentas. The effects of infection on directly infected cells and their immediate environment and indirect effects that occur at more distant sites were revealed. This information may inform the development of therapies to improve placental function after CMV infection.

immunology↗

bioRxiv2021

Discovery of Clinically Relevant Fusions in Pediatric Cancer

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. ResultsOur ensemble fusion detection approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and AWS serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the ensemble fusion-calling pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patients medical record, both known and novel fusions provided medically meaningful information. ConclusionsOur ensemble fusion detection pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

genomics↗