Biology subjects

Veta, M.

Publications and source records attributed to Veta, M..

5 recordsLinked to original sources

bioRxiv2026

Single-molecule imaging and tracking on clinical liquid biopsies reveals cancer biomarkers nanoscale organization and heterogeneity

Single-molecule imaging and tracking have revealed fundamental biological mechanisms at the molecular scale, yet their application to clinical research remains limited by technical complexity and sample preparation incompatible with patient-derived specimens. As a result, we lack information with molecular-scale resolution of clinically relevant biomarkers. Here, we develop a workflow enabling Points Accumulation in Nanoscale Topography (PAINT) combined with single-particle tracking (SPT) on clinical liquid biopsies, allowing analysis of biomarker nanoscale organization at the single-molecule level in cancer patients. Our approach features a sample preparation tailored to liquid biopsies and requires no fixation, covalent labelling, or genetic modification, making single-molecule imaging compatible with hospital clinical workflows. We demonstrate the methods diversity by imaging liquid biopsies from blood, bone marrow aspirates, and pleural effusions across different cancer types. PAINT-SPT captures both the expression and mobility of clinically relevant membrane receptor biomarkers, revealing pronounced inter- and intra-patient heterogeneity at the molecular and cellular levels. We discover that individual patients exhibit distinct molecular mobility fingerprints that reflect biomarker interaction states and correlate with clinical diagnostic readouts. Furthermore, these fingerprints distinguish healthy from cancer cells, enabling the development of a classifier that accurately identifies cancer cells based on their single-molecule behaviour. Together, our results establish a route to investigate patient-derived clinical samples at the single-molecule level and open new opportunities to understand cancer biology and biomarker function beyond ensemble-averaged measurements.

biophysics↗

bioRxiv2024

Dynamic imaging of myelin pathology in physiologically preserved human brain tissue using third harmonic generation microscopy

Myelin pathology is known to play a central role in disorders such as multiple sclerosis (MS) among others. Despite this, the pathological mechanisms underlying these conditions are often difficult to unravel. Conventional techniques like immunohistochemistry or dye-based approaches, do not provide a temporal characterization of the pathophysiological aberrations responsible for myelin changes in human specimens. Here, to circumvent this curb, we present a label-free, live-cell imaging approach of myelin using recent advancements in nonlinear harmonic generation microscopy applied to physiologically viable human brain tissue from post-mortem donors. Gray and white matter brain tissue from epilepsy surgery and post-mortem donors was excised. To sustain viability of the specimens for several hours, they were subjected to either acute or organotypic slice culture protocols in artificial cerebral spinal fluid. Imaging was performed using a femtosecond pulsed 1060 nm laser to generate second harmonic generation (SHG) and third harmonic generation (THG) signals directly from myelin and axon-like structures without the need to add any labels. Experiments on acute human brain slices and post-mortem human slice cultures reveal that myelin, along with lipid bodies, are the prime sources of THG signal. We show that tissue viability is maintained over extended periods during THG microscopy, and that prolonged THG imaging is able to detect experimentally induced subtle alterations in myelin morphology. Finally, we provide practical evidence that live-cell imaging of myelin with THG microscopy is a sensitive tool to investigate subtle changes in white matter of neurological donors. Overall, our findings support that nonlinear live-cell imaging is a suitable setup for researching myelin morphology in neurological conditions like MS.

neuroscience↗

bioRxiv2023

Advancing GABA-edited MRS Research through a Reconstruction Challenge

PurposeTo create a benchmark for the comparison of machine learning-based Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) reconstruction models using one quarter of the transients typically acquired during a complete scan. MethodsThe Edited-MRS reconstruction challenge had three tracks with the purpose of evaluating machine learning models trained to reconstruct simulated (Track 1), homogeneous in vivo (Track 2), and heterogeneous in vivo (Track 3) GABA-edited MRS data. Four quantitative metrics were used to evaluate the results: mean squared error (MSE), signal-to-noise ratio (SNR), linewidth, and a shape score metric that we proposed. Challenge participants were given three months to create, train and submit their models. Challenge organizers provided open access to a baseline U-NET model for initial comparison, as well as simulated data, in vivo data, and tutorials and guides for adding synthetic noise to the simulations. ResultsThe most successful approach for Track 1 simulated data was a covariance matrix convolutional neural network model, while for Track 2 and Track 3 in vivo data, a vision transformer model operating on a spectrogram representation of the data achieved the most success. Deep learning (DL) based reconstructions with reduced transients achieved equivalent or better SNR, linewidth and fit error as conventional reconstructions with the full amount of transients. However, some DL models also showed the ability to optimize the linewidth and SNR values without actually improving overall spectral quality, pointing to the need for more robust metrics. ConclusionThe edited-MRS reconstruction challenge showed that the top performing DL based edited-MRS reconstruction pipelines can obtain with a reduced number of transients equivalent metrics to conventional reconstruction pipelines using the full amount of transients. The proposed metric shape score was positively correlated with challenge track outcome indicating that it is well-suited to evaluate spectral quality.

bioengineering↗

bioRxiv2022

Differentiation of leukocytes in bronchoalveolar lavage fluid samples using higher harmonic generation microscopy and deep learning

In many diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. In BALF the differentiation of neutrophils, eosinophils, lymphocytes, and macrophages can contribute to diagnose the underlying ILD entity. To analyze the BALF standard cytological techniques are labor-intensive and time-consuming. Studies have shown promising cell identification performance on blood fractions analyzed by third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Here, we extend this to BALF samples, and we trained a deep learning algorithm for automated analysis on the image level against reference cytology. We imaged blood fractions from three healthy individuals and one asthma patients, and six BALFs from ILD patients. We determined the leukocyte characteristics in terms of cellular and nuclear morphology, and THG and MPEF signal intensity. A deep learning model was trained on both blood fractions and BALF 2D images was used to estimate the leukocyte ratios by using only the standard cytology differential cell ratios at the image-level as reference. The deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, coming within a 2 to 10% margin in BALF samples in the hold-out testing set. We suggest that the performance of the combined label free imaging and AI analysis can be improved further by collecting 3D data and data of additional fluid samples of various ILD diseases and healthy samples, THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios will not only speed-up the diagnostic process but can also reduce costs, the workload and reduce inter-observer variations.

immunology↗

bioRxiv2019

Automated Clear Cell Renal Carcinoma Grade Classification with Prognostic Significance

We developed an automated 2-tiered Fuhrmans grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n=72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrmans grades between TCGA and Pathologist 1 (training set n=235; held-out test set n=42). Discordant cases (n=118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n=160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21-3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.

pathology↗