Biology subjects

Migliori, B.

Publications and source records attributed to Migliori, B..

7 recordsLinked to original sources

bioRxiv2023

Patient iPSC models reveal glia-intrinsic phenotypes in multiple sclerosis

Multiple sclerosis (MS) is considered an inflammatory and neurodegenerative disease of the central nervous system, typically resulting in significant neurological disability that worsens over time. While considerable progress has been made in defining the immune systems role in MS pathophysiology, the contribution of intrinsic CNS-cell dysfunction remains unclear. Here, we generated the largest reported collection of iPSC lines from people with MS spanning diverse clinical subtypes and differentiated them into glia-enriched cultures. Using single-cell transcriptomic profiling, we observed several distinguishing characteristics of MS cultures pointing to glia-intrinsic disease mechanisms. We found that iPSC-derived cultures from people with primary progressive MS contained fewer oligodendrocytes. Moreover, iPSC-oligodendrocyte lineage cells and astrocytes from people with MS showed increased expression of immune and inflammatory genes that match those of glial cells from MS postmortem brains. Thus, iPSC-derived MS models provide a unique platform for dissecting glial contributions to disease phenotypes independent of the peripheral immune system and identify potential glia-specific targets for therapeutic intervention.

neuroscience↗

bioRxiv2023

FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array(R). The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 seconds per 96-well plate, and achieves this result even in heavily downsampled data ([~]30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.

bioinformatics↗

bioRxiv2023

ScaleFExSM: a lightweight and scalable method to extract fixed features from single cells in high-content imaging screens

Leveraging artificial intelligence (AI) in image-based morphological profiling of cell populations is proving increasingly valuable for identifying diseased states and drug responses in high-content imaging (HCI) screens. When the differences between populations (such as a healthy and diseased) are completely unknown and undistinguishable by the human eye, it is crucial that HCI screens are large in scale, allowing numerous replicates for developing reliable models, as well as accounting for confounding factors such as individual (donor) and intra-experimental variation. However, as screen sizes increase, challenges arise including the lack of scalable solutions for analyzing high-dimensional datasets and processing the results in a timely manner. For this purpose, many tools have been developed to reduce images into a set of features using unbiased methods, such as embedding vectors extracted from pre-trained neural networks or autoencoders. While these methods preserve most of the predictive power contained in each image despite reducing the dimensionality significantly, they do not provide easily interpretable information. Alternatively, techniques to extract specific cellular features from data are typically slow, difficult to scale, and often produce redundant outputs, which can lead to the model learning from irrelevant data, which might distort future predictions. Here we present ScaleFExSM, a memory efficient and scalable open-source Python pipeline that extracts biologically meaningful features from large high-content imaging datasets. It requires only modest computational resources but can also be deployed on high-powered cloud computing infrastructure. ScaleFExSM can be used in conjunction with AI models to cluster data and subsequently explore, identify, and rank features to provide insights into the morphological hallmarks of the phenotypic categories. We demonstrate the performance of this tool on a dataset consisting of control and drug-treated cells from a cohort of 20 donors, benchmarking it against the state-of-the-art tool, CellProfiler, and analyze the features underlying the phenotypic shift induced by chemical compounds. In addition, the tools generalizability and utility is shown in the analysis of publicly available datasets. Overall, ScaleFExSM constitutes a robust and compact pipeline for identifying the effects of drugs on morphological phenotypes and defining interpretable features that can be leveraged in disease profiling and drug discovery.

cell biology↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗

bioRxiv2019

Cortical Overgrowth in a Preclinical Forebrain Organoid Model of CNTNAP2-Associated Autism Spectrum Disorder

Autism spectrum disorder (ASD) represents a major public health burden but translating promising treatment findings from preclinical non-human models of ASD to the clinic has remained challenging. The recent development of forebrain organoids generated from human induced pluripotent stem cells (hiPSCs) derived from subjects with brain disorders is a promising method to study human-specific neurobiology, and may facilitate the development of novel therapeutics.\n\nIn this study, we utilized forebrain organoids generated from hiPSCs derived from patients from the Old Order Amish community with a rare syndromic form of ASD, carrying a homozygous c.3709DelG mutation in CNTNAP2 and healthy controls to investigate the effects of this mutation on cortical embryonic development.\n\nPatients carrying the c.3709DelG mutation in CNTNAP2 present with an increased head circumference and brain MRI reveals an increase in gray matter volume. Patient-derived organoids displayed an increase in total volume that was driven by an increased proliferation in neural progenitor cells, leading to an increase in the generation of cortical neuronal and non-neuronal cell types. The observed phenotypes were rescued after correction of the pathogenic mutation using CRISPR-Cas9. RNA sequencing revealed 339 genes differentially expressed between patient- and control-derived organoids of which a subset are implicated in cell proliferation and neurogenesis. Notably, these differentially expressed genes included previously identified ASD-associated genes and are enriched for genes in ASD-associated weighted gene co-expression networks.\n\nThis work provides a critical step towards understanding the role of CNTNAP2 in human cortical development and has important mechanistic implications for ASD associated with brain overgrowth. This CNTNAP2 organoid model provides opportunity for further mechanistic inquiry and development of new therapeutic strategies for ASD.

neuroscience↗

bioRxiv2019

Live detection of neural stem and glioblastoma cells by an oligothiophene derivative

Here we report a luminescent conjugated oligothiophene (LCO), named p-HTMI, for non-invasive and non-amplified real-time detection of live human patient-derived glioblastoma (GBM) cells and embryonic neural stem cells (NSCs). While p-HTMI stained only a small fraction of other cell types investigated, the mere addition of p-HTMI to the cell culture resulted in efficient detection of NSCs or GBM cells from rodents and humans within minutes. p-HTMI is functionalized with a methylated imidazole moiety resembling the side chain of histidine/histamine, and non-methylated analogues were not functional. p-HTMI were readily able to detect a subpopulation of human cells in vivo in mouse brain sections with tumors developed from orthotopically transplanted patient-derived GBM cells. Cell sorting experiments of human GBM cells demonstrated that p-HTMI labeled the same population as CD271, a proposed marker for stem cell-like cells and rapidly migrating cells in glioblastoma. Our results suggest that the LCO p-HTMI is a versatile tool for immediate and selective detection of subpopulations of neural stem and glioma cells.

bioengineering↗

bioRxiv2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

neuroscience↗