Biology subjects

Migliorini, A.

Publications and source records attributed to Migliorini, A..

4 recordsLinked to original sources

bioRxiv2026

Extra-lineage tissue programs define the transcription states of human pancreatic cancer

Cancers acquire alternate transcriptional states as they evolve, but the origins, timing and determinants of this plasticity are poorly understood in many tumours. We investigated the transcriptional states of pancreatic cancer by integrating [~]1000 tumour-enriched genomes and transcriptomes from 464 patients combined with scRNA-seq, multiome profiling, and spatial proteomics. Four epithelial states covering the spectrum of lineage plasticity were identified (Classical-1, Classical-2, Basal-1, Basal-2). Comparing these states to normal and pan-cancer human single cell atlases showed each state reflects distinct tissue programs found in other malignancies. Single cell analysis uncovered that the main transcription state of this disease (Classical-1) emerges before KRAS mutations. Spatial proteomics from patients and cancer-free donors showed that the Classical-1 program emerges during acinar-to-ductal metaplasia, and also unexpectedly, in normal ducts without disrupting their morphology. Overall, these findings link the extensive lineage plasticity potential of this organ to the origins of the transcriptional states.

cancer biology↗

bioRxiv2024

Development of an on-chip fluorescence anisotropy immunoassay for human C-peptide secretion reveals a general roadmap for tracer optimization

Fluorescence anisotropy immunoassays (FAIAs) are widely used to quantify the concentration of target proteins based on competition with a tracer in binding a monoclonal antibody. We recently designed an FAIA to measure mouse C-peptide secretion from living islets in a continuous-flow microfluidic device (InsC-chip). To develop an assay for human C-peptide, our initial selection of antibody-tracer pairings revealed the need to optimize both the dynamic range and the binding kinetics to measure the assay on-chip effectively. Here, we present strategies for developing an on-chip FAIA using two different monoclonal antibodies to achieve both a large dynamic range and high temporal resolution. The two monoclonal antibodies (Ab1 & Ab2) to human C-peptide initially showed low dynamic range and slow kinetics, preventing them from being used in an on-chip assay. To shorten the time-to-reach equilibrium for Ab1, we reengineered the tracer based on a comparison between the human and mouse C-peptide sequences, resulting in > 30-fold shorter time-to-reach equilibrium. To increase the relatively small dynamic range for Ab2, we used partial epitope mapping and targeted point mutations to increase the dynamic range by 45%. Finally, we validated both FAIAs by measuring depolarization-induced insulin secretion from individual hESC-islets in our InsC-chip. These strategies provide a general roadmap for developing FAIAs with high sensitivity and sufficiently fast kinetics to be measured in continuous-flow microfluidic devices.

bioengineering↗

bioRxiv2023

Macrophages heterogeneity and significance during human fetal pancreatic development

Organogenesis is a complex process that relies on a dynamic interplay between extrinsic factors originating from the microenvironment and intrinsic factors specific to the tissue. For the endocrine cells of the islet of Langerhans, the local microenvironment consists of various cell types including pancreatic acinar and ductal cells as well as neuronal, immune, endothelial, and stromal cells. Interestingly, hematopoietic cells have been detected in human pancreas as early as 6 post-conception weeks (PCW)1,2, but whether they play a role during islet formation in humans remains largely unknown. To shed light on this question, we performed single nuclei RNA sequencing of the human fetal pancreas during the early weeks of the second trimester, specifically focusing on the molecular interaction between the hematopoietic niche and the pancreatic epithelium. Our analysis identified a wide range of hematopoietic cells as well as two distinct subsets of macrophages that are unique to the fetal pancreas and absent in neonatal or adult pancreatic tissues. Leveraging this discovery, we developed a co-culture system of hESC-derived endocrine-macrophage organoids to model their interaction in vitro. Remarkably, we found that macrophages promoted the differentiation and viability of developing endocrine cells in vitro and enhanced tissue engraftment in immunocompromised mice, supporting a role for these cells in future tissue engineering strategies for diabetes.

developmental biology↗

bioRxiv2022

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Multiple pancreatic islet single-cell RNA sequencing (scRNA-seq) datasets have been generated to study development, homeostasis, and diabetes. However, there is no consensus on cell states and pathways across conditions as well as the value of preclinical mouse models. Since these challenges can only be resolved by jointly analyzing multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including an autoimmune type 1 diabetes (T1D) model (NOD), a gluco-/lipotoxicity T2D model (db/db), and a chemical streptozotocin (STZ) {beta}-cell ablation model. MIA is a curated resource for interactive exploration and computational querying, providing new insights inaccessible from individual datasets. The {beta}-cell landscape of MIA revealed new disease progression cell states and cross-publication differences between previously suggested marker genes. We show that in the STZ model {beta}-cells transcriptionally correlate to human T2D and mouse db/db, but are less similar to human T1D and mouse NOD. We observe different pathways shared between immature, aged, and diabetes model {beta}-cells. In conclusion, our work presents the first comprehensive analysis of {beta}-cell responses to different stressors, providing a roadmap for the understanding of {beta}-cell plasticity, compensation, and demise.

bioinformatics↗