Biology subjects

Hochner, H.

Publications and source records attributed to Hochner, H..

4 recordsLinked to original sources

bioRxiv2025

Biologically-Inspired Digital Histology for Deep Phenotyping of Placental Composition Changes Across Major Lesion Types

Placenta pathology provides diagnostic insights for understanding pregnancy complications and guides maternal and perinatal care. While placental abnormalities reflect both acute and chronic maternal-fetal health states, the organs spatial and temporal heterogeneity poses significant challenges for systematic histological analysis. To date, there has been no analysis of cell populations and their relationship to placental lesions due to the scale of annotations required by expert pathologists. The Histology Analysis Pipeline.PY (HAPPY), previously published by our group, is a hierarchical deep learning approach for quantitative single-cell resolution analysis of H&E slides. Here we apply HAPPY to analyse 130 slides from 62 live full-term pregnancies across healthy controls and four common placental lesion types (infarction, perivillous fibrin, avascular villi, and intervillous thrombosis). We computed cell-type and tissue-structure compositions, established the expected range of healthy variation, and quantified slide-level deviation from this reference using compositional data analysis. Our results reveal significant cellular composition differences between histologically normal and lesioned placentas, including increased extra-villous trophoblasts and leukocytes, coupled with decreases in Hofbauer cells. These changes accompanied distinctive tissue microstructural alterations, particularly increased fibrin deposition and changes to the villous structures. The magnitude of compositional deviation increased with infarction size but not with intervillous thrombosis. Importantly, many differences extend beyond visibly affected areas, indicating organ-wide adaptive responses rather than purely discrete focal pathologies. This quantitative characterisation provides insights into relationships between specific pathologies and placental structure, demonstrating the potential of AI-based methods to enhance conventional histopathological assessment for research and clinical practice.

pathology↗

bioRxiv2024

Time is encoded by methylation changes at clustered CpG sites

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent DNA methylation changes are regional and occur at multiple adjacent CpG sites, either stochastically or in a coordinated block-like manner. Deep learning analysis of single-molecule patterns in two genomic loci achieved accurate age prediction with a median error of 1.46-1.7 years on held-out human blood samples, dramatically improving current epigenetic clocks. Factors such as gender, BMI, smoking and other measures of biological aging do not affect chronological age inference. Longitudinal 10-year samples revealed that early deviations from epigenetic age are maintained throughout life and subsequent changes faithfully record time. Lastly, the model inferred chronological age from as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues, and facilitate medical and forensic applications. O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/626674v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c4dd52org.highwire.dtl.DTLVardef@9e6e62org.highwire.dtl.DTLVardef@160f8c3org.highwire.dtl.DTLVardef@16be7a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images

Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placentas heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the Histology Analysis Pipeline.PY (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPYs cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.

bioinformatics↗

bioRxiv2017

A study of Kibbutzim in Israel reveals risk factors for cardiometabolictraits and subtle population structure

Genetic studies in isolated populations have provided increased power for identifying loci associated with complex diseases and traits. We present here the Kibbutzim Family Study (KFS), initiated for investigating environmental and genetic determinants of cardiometabolic traits in extended Israeli families living in communes characterized by long-term social stability and homogeneous environment. Extensive information on cardiometabolic traits, as well as genome-wide genetic data, was collected on 901 individuals, making this study, to the best of our knowledge, the largest of its kind in Israel. We have thoroughly characterized the KFS genetic structure, observing that most participants were of Ashkenazi Jewish (AJ) origin, and confirming a recent severe bottleneck in their recent history (point estimates: effective size {approx}450 individuals, 23 generations ago). Focusing on genetic variants enriched in KFS compared with non-Finnish Europeans, we demonstrated that AJ-specific variants are largely involved in cancer-related pathways. Using linear mixed models, we conducted an association study of these enriched variants with 16 cardiometabolic traits. We found 24 variants to be significantly associated with cardiometabolic traits. The strongest association, which we also replicated, was between a variant upstream of the MSRA gene, {approx}200-fold enriched in KFS, and weight (P=3.6{middle dot}10-8). In summary, the KFS is a valuable resource for the study of the population genetics of Israel as well as the genetics of cardiometabolic traits in a homogeneous environment.

genetics↗