Biology subjects

Dagan, S.

Publications and source records attributed to Dagan, S..

3 recordsLinked to original sources

bioRxiv2023

CoSMIC - A hybrid approach for large-scale, high-resolution microbial profiling of novel niches

Standard microbial profiling based on 16S rRNA (16S) sequencing suffers from a lack of primer universality, primer biases and often yields low resolution. We introduce Comprehensive Small Ribosomal Subunit Mapping and Identification of Communities (CoSMIC), addressing these challenges, especially in unexplored niches. CoSMIC begins with long-read sequencing of the full-length 16S gene, amplified by generic Locked Nucleic Acid primers over pooled samples, thus augmenting reference databases with novel niche-specific gene sequences. Subsequently, CoSMIC amplifies multiple non-consecutive variable regions along the gene, followed by short-read sequencing of each sample. Data from the different regions are integrated using the SMURF framework, alleviating primer biases and providing de-facto full gene resolution. Using a mock community, CoSMIC identified full-length 16S genes with significantly higher specificity and sensitivity while dramatically increasing resolution compared to standard methods. Evaluating CoSMIC across environmental samples provided higher accuracy and unprecedented resolution while detecting thousands of novel full-length 16S sequences.

molecular biology↗

bioRxiv2022

A novel deep learning pipeline for cell typing and phenotypic marker quantification in multiplex imaging

BackgroundMultiplex immunofluorescence (mIF) can provide invaluable insights into spatial biology and the complexities of the immune tumor microenvironment (iTME). However, existing analysis approaches are both laborious and highly user-dependent. In order to overcome these limitations we developed a novel, end-to-end deep learning (DL) pipeline for rapid and accurate analysis of both tumor-microarray (TMA) and whole slide mIF images. MethodsOur pipeline consists of two DL models: a multi-classifier for classifying multi-channel cell images into 12 different cell types, and a binary classifier for determining the positivity of a given marker in single-channel images. The DL multi-classifier was trained on 7,000 tiles labeled with cell annotations from a publicly available CODEX dataset, consisting of 140 tissue cores from 35 colorectal cancer (CRC) patients. For the binary classifier training, the multi-channel tiles were further split into [~]100,000 single-channel tiles, for which the ground truth was inferred from the known expression of these markers in each cell-type. This DL binary classifier was then utilized to quantify the positivity of various cell state (phenotypic) markers. In addition, the binary classifier was exploited as a cell-typing tool, by predicting the positivity of individual lineage cell markers. The performance of our DL models was evaluated on 1,800 annotations from 14 test tissue cores. The models were further evaluated on a new 6-plex melanoma cohort, stained with PhenoImager(R), and were compared to the performance of clustering, manual thresholding or machine learning-based cell-typing methods applied on the same test sets. ResultsOur DL multi-classifier achieved highly accurate results, outperforming all of the tested cell-typing methods, including clustering, manual-thresholding and ML-based approaches, in both CODEX CRC and PhenoImager melanoma cohorts (accuracy of 91% and 87%, respectively), with F1-scores above 80% in the vast majority of cell types. Our DL binary classifier, which was trained solely on the lineage markers of the CRC dataset, also outperformed existing methods, demonstrating excellent F1-scores (>80%) for determining the positivity of unseen phenotypic and lineage markers across the two tumor types and imaging modalities. Notably, as little as 20 annotations were required in order to boost the performance on an unseen dataset to above 85% accuracy and 80% F1-scores. As a result, the DL binary classifier could successfully be used as a cell-typing model, in a manner that is transferable between experimental approaches. ConclusionsWe present a novel state-of-the-art DL-based framework for multiplex imaging analysis, that enables accurate cell typing and phenotypic marker quantification, which is robust across markers, tumor indications, and imaging modalities.

immunology↗

bioRxiv2018

Organ transformation by environmental disruption of epigenetic memory

Despite major progress in mechanistic understanding of epigenetic reprogramming of cells, the basis of organ reprograming by (epi-)gene-environment interactions remained largely obscured. Here we use the ether-induced haltere-to-wing transformations as a model for epigenetic "reprogramming" at the whole organism level. Our findings support a mechanistic chain of events explaining why and how brief embryonic exposure to ether leads to organ transformation manifested at the larval stage and on. We show that ether interferes with protein integrity in the egg leading to altered deployment of Hsp90 and repression of Trithorax-mediated establishment of H3K4 tri-methylations. This repression pre-disposes early methylated Ubx targets and wing genes for later up-regulation in the larval haltere disc, hence the wing-like outcome. Consistent with compromised protein integrity during the exposure, the severity of bithorax transformation is increased by genetic or chemical reduction of Hsp90 function. Moreover, a joint reduction in Hsp90 and trx gene dosage can cause bithorax transformations without exposure to ether. These findings implicate environmental disruption of protein integrity at the onset of histone methylations with a modification of epigenetic memory, which in turn, supports a morphogenetic shift towards an ancestral-like body plan. The morphogenetic impact of chaperone response during a major setup of epigenetic patterns may be a general scheme for organ reprogramming by environmental cues.

developmental biology↗