Biology subjects

Ahadi, S.

Publications and source records attributed to Ahadi, S..

5 recordsLinked to original sources

bioRxiv2025

The Current Landscape of Plasma Proteomics: Technical Advances, Biological Insights, and Biomarker Discovery

Plasma is a rich source of biomolecules, including proteins, that reflect both health and disease. Due to their key roles in biological processes, proteins hold significant potential as biomarkers, fueling the rise of plasma proteome profiling in recent years. Despite widespread adoption, few studies have directly compared different plasma proteomics platforms, particularly those using mass spectrometry. Our study provides a comprehensive comparison of seven platforms across three leading technologies - SomaLogic, Olink, and Mass Spectrometry (MS) - including affinity-based approaches and various MS techniques, covering over 13,000 proteins. By applying these methods to the same cohort, we assess their performance, revealing key differences and complementary strengths. Our findings offer valuable insights for researchers, highlighting trade-offs in coverage and their implications for biomarker discovery and clinical applications. This study serves as an essential resource, offering both technical evaluation and biological insights to support the development of novel diagnostics and therapeutics through plasma proteomics.

bioinformatics↗

bioRxiv2022

Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock

Biological age, distinct from an individuals chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Deep learning approaches on imaging datasets of the eye have proven powerful for a variety of quantitative phenotype inference tasks and provide an opportunity to explore organismal aging and tissue health. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals chronological age. These predictions led to the concept of a retinal aging clock, "eyeAge", which we employed for a series of downstream longitudinal analyses. eyeAge was used to predict chronological age on timescales under a year using longitudinal fundus imaging data from a subset of patients. To further validate the model, it was applied to a separate cohort from the UK Biobank. The difference between individuals eyeAge and their chronological age, hereafter "eyeAgeAccel", was computed and used for genome-wide association analysis (GWAS). EyeAge predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UKBiobank, respectively). Additionally, eyeAgeAccel was highly independent of blood marker-based measures of biological age (e.g. "phenotypic age"), maintaining an all-cause mortality hazard ratio of 1.026 even in the presence of phenotypic age. Longitudinal studies showed that the resulting models were able to predict individuals aging, in time-scales less than a year, with 71% accuracy. The individual-specific component to this prediction was confirmed with the identification of multiple GWAS hits in the independent UK Biobank cohort. The knockdown of the fly homolog to the top hit, ALKAL2, which was previously shown to extend lifespan in flies, also slowed age-related decline in vision in flies. In conclusion, predicted age from retinal images can be used as a biomarker of biological aging that is independent from assessment based on blood markers. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.

bioinformatics↗

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↗

bioRxiv2020

Molecular Choreography of Acute Exercise

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

systems biology↗

bioRxiv2018

DIAlign provides precise retention time alignment across distant runs in DIA and targeted proteomics

SWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.\n\nAbbreviations\n\nData AvailabilityRaw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.\n\nServername: ftp.peptideatlas.org\n\nUsername: PASS01280\n\nPassword: KQ2592b

bioinformatics↗