Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling.

Understanding the spatial heterogeneity of tumours and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin and eosin and serial immunohistochemistry staining, a cumbersome, tissue-exhaustive process that results in non-aligned tissue images. We propose the VirtualMultiplexer, a generative artificial intelligence toolkit that effectively synthesizes multiplexed immunohistochemistry images for several antibody markers (namely AR, NKX3.

Overcoming limitations in current measures of drug response may enable AI-driven precision oncology.

Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.

ScLinear predicts protein abundance at single-cell resolution.

Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise.

scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data.

With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses.

ATHENA: analysis of tumor heterogeneity from spatial omics measurements.

Summary: Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements.

Quantification of tumor heterogeneity: from data acquisition to metric generation.

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization.

Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity.

A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture.

analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity.

DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion across a population. Disruptions in these mechanisms lead to DNA re-replication, closely connected to genomic instability and oncogenesis. Here, we present a stochastic hybrid model of DNA re-replication that accurately portrays the interplay between discrete dynamics, continuous dynamics and uncertainty.

A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.

Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry.

EasyFRAP-web: a web-based tool for the analysis of fluorescence recovery after photobleaching data.

Understanding protein dynamics is crucial in order to elucidate protein function and interactions. Advances in modern microscopy facilitate the exploration of the mobility of fluorescently tagged proteins within living cells. Fluorescence recovery after photobleaching (FRAP) is an increasingly popular functional live-cell imaging technique which enables the study of the dynamic properties of proteins at a single-cell level.

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data.

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course.

Analysis of Protein Kinetics Using Fluorescence Recovery After Photobleaching (FRAP).

Fluorescence recovery after photobleaching (FRAP) is a cutting-edge live-cell functional imaging technique that enables the exploration of protein dynamics in individual cells and thus permits the elucidation of protein mobility, function, and interactions at a single-cell level. During a typical FRAP experiment, fluorescent molecules in a defined region of interest within the cell are bleached by a short and powerful laser pulse, while the recovery of the fluorescence in the region is monitored over time by time-lapse microscopy. FRAP experimental setup and image acquisition involve a number of steps that need to be carefully executed to avoid technical artifacts.

IPP Complex Reinforces Adhesion by Relaying Tension-Dependent Signals to Inhibit Integrin Turnover.

Cytoskeleton-mediated forces regulate the assembly and function of integrin adhesions; however, the underlying mechanisms remain unclear. The tripartite IPP complex, comprising ILK, Parvin, and PINCH, mediates the integrin-actin link at Drosophila embryo muscle attachment sites (MASs). Here, we demonstrate a developmentally earlier function for the IPP complex: to reinforce integrin-extracellular matrix (ECM) adhesion in response to tension.

CK1δ restrains lipin-1 induction, lipid droplet formation and cell proliferation under hypoxia by reducing HIF-1α/ARNT complex formation.

Proliferation of cells under hypoxia is facilitated by metabolic adaptation, mediated by the transcriptional activator Hypoxia Inducible Factor-1 (HIF-1). HIF-1α, the inducible subunit of HIF-1 is regulated by oxygen as well as by oxygen-independent mechanisms involving phosphorylation. We have previously shown that CK1δ phosphorylates HIF-1α in its N-terminus and reduces its affinity for its heterodimerization partner ARNT.

Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments.

Motivation: Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, a number of analytical models have been developed. Simplifications are inherent in these models, which may lead to inexhaustive or inaccurate exploitation of the experimental data.

Multi-step loading of human minichromosome maintenance proteins in live human cells.

Once-per-cell cycle replication is regulated through the assembly onto chromatin of multisubunit protein complexes that license DNA for a further round of replication. Licensing consists of the loading of the hexameric MCM2-7 complex onto chromatin during G1 phase and is dependent on the licensing factor Cdt1. In vitro experiments have suggested a two-step binding mode for minichromosome maintenance (MCM) proteins, with transient initial interactions converted to stable chromatin loading.

easyFRAP: an interactive, easy-to-use tool for qualitative and quantitative analysis of FRAP data.

Summary: We present easyFRAP, a versatile tool that assists quantitative and qualitative analysis of fluorescence recovery after photobleaching (FRAP) data. The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.

A de novo 8q22.2-24.3 duplication in a patient with mild phenotype.

We report a new case of 8q interstitial duplication in a patient with dysmorphic features, umbilical hernia, cryptorchidism, short stature, congenital heart defect and mild mental retardation (MR). Chromosome analysis with high resolution QFQ bands showed 46,XY, 8q+, which was interpreted as a partial duplication of the distal long arm of chromosome 8 (q22 → qter). This chromosomal aberration was further characterized using fluorescence in situ hybridization (FISH) analyses with multiple DNA probes and array-CGH (Comparative Genomic Hybridization) experiment which demonstrated a de novo direct duplication (8)(q22.

Co-existence of phenylketonuria and Fabry disease on a 3 year-old boy: case report.

Background: The co-existence of two genetically distinct metabolic disorders in the same patient has rarely been reported. Phenylketonuria (PKU) is an inborn error of the metabolism resulting from a phenylalanine hydroxylase deficiency. Fabry disease (FD) is an X-linked lysosomal storage disorder due to a deficiency of the enzyme alpha-galactosidase A.

Serum prolactin as a tool for the follow-up of treated DHPR-deficient patients.

Deficiency of dihydropteridine reductase causes a variant form of phenylketonuria associated with a devastating neurological disease characterized by mental retardation, hypokinesis and other features relating to basal ganglia disorder. Hyperphenylalaninaemias with tetrahydrobiopterin deficiency make up about 1-3% of all hyperphenylalaninaemias. We describe three patients from Calabria, a southern region of Italy, who have a dihydropteridine reductase deficiency, caused by the same mutation (p.