Biology subjects

Bentolila, L.

Publications and source records attributed to Bentolila, L..

5 recordsLinked to original sources

bioRxiv2026

Correlative SHG-AFM imaging workflow for label-free quantitative analysis of collagen structure-function relationships

We present a user-friendly correlative second harmonic generation (SHG) and atomic force microscopy (AFM) imaging workflow for quantifying the nanomechanical properties of collagen in unfixed, unlabeled tissue sections. SHG Aligned Profiling for Elasticity and Segmentation or SHAPES utilizes SHG imaging to guide AFM force mapping, enabling label-free, anatomically specific selection of regions of interest, facilitating spatially resolved characterization of fibrous collagen morphology and local stiffness. Compared to brightfield or confocal contrast-guided AFM mapping, SHG improves anatomical specificity without fixation or staining, enabling downstream analysis on the same tissue section while preserving spatial correspondence between structural (SHG) and mechanical (AFM) data. Rapid identification of regions of interest also reduces the risk of costly AFM tip breakage, improving throughput and reducing operator burden. Utilizing only standard turn-key commercial systems common in many user facilities and clinical laboratories, multimodal image coregistration and automated identification of anatomical regions of interest are employed to integrate SHG and AFM datasets across complex sample topographies. Coregistration of SHG and AFM images substantially increases the number of usable datasets per measurement session, facilitating translation of these complementary modalities and bridging nanomechanical imaging with clinical research practice.

biophysics↗

bioRxiv2025

Transverse Sheet Illumination Microscopy (TranSIM): A High-Speed Platform for Large-Scale Volumetric Imaging

Recording the neural activity of biological organisms is paramount in understanding how they process the world around them. Fluorescence microscopy has served as the standard in recording this neural activity due to its ability to capture large populations of neurons simultaneously. Recent efforts in fluorescence microscopy have been concentrated in imaging large scale volumes, however, most of these efforts have been limited by spatiotemporal and bandwidth constraints. We present a novel system called Transverse-Sheet Illumination Microscopy (TranSIM), which captures spatially separated planes onto multiple two-dimensional sCMOS sensors at near diffraction limited resolution with 1.0 {micro}m, 1.4 {micro}m, and 4.3 {micro}m (x, y, and z respectively). The parallel use of sensors reduces the bandwidth bottlenecks typically found in other systems. TranSIM allows for the capturing of data at large-scale volumetric field-of-views up to 748 x 278 x 100 {micro}m3 at 100 Hz. Moreover, we were able to capture smaller field-of-views of 374 x 278 x 100 {micro}m3 at a faster volumetric rate of 200 Hz. Additionally, we found that the systems versatile design allowed us the ability to change the vertical magnification programmatically rather than necessitating a change of objectives. With this system we were able to observe intricate communication between neuron populations separated by vast three-dimensional distances, raising the potential to answer complex questions in Neurobiology.

bioengineering↗

bioRxiv2024

Development of a LRRC15-Targeted Radio-Immunotheranostic Approach to Deplete Pro-tumorigenic Mechanisms and Immunotherapy Resistance

Leucine-rich repeat containing 15 (LRRC15) has emerged as an attractive biomarker and target for cancer therapy. We have developed a humanized monoclonal antibody (mAb), DUNP19, that specifically binds to a phylogenetically conserved LRRC15 epitope and is internalized by target-expressing cancer and stromal cells. In xenograft mouse models, Lutetium-177 labeled DUNP19 ([177Lu]-DUNP19) enables non-invasive imaging and precise radiotherapy to LRRC15-expressing cancer cells and murine cancer-associated fibroblasts (CAFs), halting tumor progression and prolonging survival with minimal toxicity. Transcriptomic analyses of [177Lu]-DUNP19-treated tumors reveal a loss of pro-tumorigenic mechanisms, including a transforming growth factor beta (TGF{beta})-driven and LRRC15+ signature associated with immunotherapy resistance. Together, these results demonstrate that radio-theranostic targeting of LRRC15 with DUNP19 is a compelling precision medicine platform for image-guided diagnosis, eradication, and reprogramming of LRRC15+ tumor tissue that drives immuno-resistance and aggressive disease. SIGNIFICANCEWe introduce a pioneering LRRC15-guided radio-theranostic approach integrating clinical imaging and radioimmunotherapy. Our strategy utilizes a mAb, DUNP19, to target LRRC15-expressing cancer cells and fibroblasts, demonstrating significant tumor reduction, prolonged survival, and reversal of TGF{beta}-driven treatment resistance. This approach offers a promising strategy for improving outcomes in aggressive cancers.

pharmacology and toxicology↗

bioRxiv2021

Growth rate as a modulator of tooth patterning during adaptive radiations

Tooth classes are a mammalian innovation that has contributed to the evolutionary success of mammals. However, our understanding of how tooth classes have evolved and diversified remains limited. Here, we use the evolutionary radiation of noctilionoid bats, the most diverse clade of mammals in terms of diet type, as a model system to show how the tooth developmental program evolved during the adaptation to new diet types. We combined morphological, developmental, cellular, and modeling approaches, to investigate the developmental differences between two tooth classes, molars and premolars and the mechanisms driving their diversification. We demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. Then we showed that the dramatic diversification of tooth number and size is driven by the modulation of the growth rate of the jaw, explaining the rapid gain/loss of teeth during the evolution of this clade. Finally, we propose a mathematical model that recapitulates the successive appearance of tooth buds and supports the hypothesis that growth acts as a key driver of the evolution of tooth number and size by tinkering with reaction/diffusion processes. Our results demonstrate developmental independence between mammalian tooth classes and provide a mechanism to explain their rapid diversification. More broadly, these results reveal how simple modifications of one developmental mechanism by another can drive the evolution of repeated structures during adaptive radiations.

evolutionary biology↗

bioRxiv2018

Deep learning achieves super-resolution in fluorescence microscopy

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

bioengineering↗