Biology subjects

Cohen, Z.

Publications and source records attributed to Cohen, Z..

11 recordsLinked to original sources

bioRxiv2026

Folding scFv--Antigen Complexes at Scale

AO_SCPLOWBSTRACTC_SCPLOWAccurate modeling of antibody-antigen (Ab-Ag) complexes is central to biologic development, yet the reliability and failures of modern Ab-Ag folding pipelines remain poorly characterized. Single-chain variable fragments (scFvs) are thera-peutically important antibodies, but large-scale evaluations of structure prediction models on scFv-Ag complexes are largely lacking. We introduce a scalable bench-marking pipeline that generates large ensembles of scFv-Ag structure predictions by cofolding a curated subset of 3,800 Ab-Ag complexes from SAbDab using multiple state-of-the-art models under diverse inference-time settings. The resulting dataset, SCALE (scFv-Ag CompLex Ensembles), includes standardized scFv-Ag sequences and around 200,000 predicted complexes spanning different models, sampling strategies, and auxiliary inputs. Using SCALE, we evaluate model performance in recovering correct scFv-Ag interfaces and assess the ability of existing confidence metrics to select the best structure from prediction ensembles. We find that while confidence scores effectively distinguish easy from hard scFv-Ag complexes, they often fail to identify the highest-quality interface for a given target. Further analysis shows that near-correct interfaces typically appear in ensembles but at low frequency, and inference-time choices such as sampling, recycling, and using evolutionary or structural information are crucial for accurate scFv-Ag complex predictions. Dataset and analysis code are available at https://huggingface.co/datasets/ravishah1/SCALE

bioinformatics↗

bioRxiv2026

Optical Spectral Fingerprinting Enables Sensitive Detection of Anthracycline Chemotherapeutics in Synthetic Clinical Biofluids

Anthracycline chemotherapeutics are commonly used as frontline treatments for a wide array of cancers. However, their administration to patients results in substantial side effects, primarily cardiotoxicity, as well as myelosuppression and gastrointestinal toxicity. Current clinical management of such side effects is solely based on a lifetime dosage limit, which inhibits their anti-tumor efficacy. Many individualized factors, including age, family history of cardiovascular disease, treatment regimen, and other co-morbidities influence drug pharmacology. Despite this heterogeneity, there is no method for determining actual organ or tumor exposure to the treatment in an individual. Here, we developed an optical nanosensor array for four anthracyclines--doxorubicin, daunorubicin, epirubicin, and idarubicin. We used single-walled carbon nanotubes as the signal transducer due to their tunable near-infrared fluorescence. We screened twelve distinct ssDNA sequences paired with seven SWCNT (n,m) species at increasing concentrations of each of the four anthracyclines. The spectral responses were then used to develop machine learning-based classification models to identify different anthracycline types and concentrations. The optimized extreme gradient boosting model was able to classify high levels of each anthracycline with 100% accuracy. Concentration-based classification by PCA was performed for each anthracycline, distinguishing low ([≤] 5 {micro}M) and high (> 5 {micro}M) concentrations. Finally, we validated the sensor performance using synthetic urine and sweat. Our findings demonstrate the potential of carbon nanotube-based sensor array to measure the pharmacokinetics of anthracyclines in patients with the goal of enhancing anti-tumor efficacy and monitoring off-target toxicities.

bioengineering↗

bioRxiv2026

High-throughput SWCNT NIR-II screening enables cell and in vivo applications

Diagnostic sensor development and clinical translation lag, in part, due to a lack of rapid, high-throughput screening methodology. Optimization of high-throughput sensor development and deployment will likely help in expediting tools toward the clinic. To address this issue, we optimized high-throughput screening parameters using a near-infrared (NIR-II) plate reader attached to an external probe for in vivo testing. We assessed spectroscopy parameters to improve speed and precision in screening a single-walled carbon nanotube (SWCNT)-based optical sensor. To do so, we assessed the appropriate well plate specifications, including laser power, excitation wavelength, exposure time, and focal height parameters for SWCNT-based optical sensor development. We also used the plate reader to screen fluorescent SWCNT which were endocytosed by a macrophage cell line. We then performed NIR probe spectroscopy to assess SWCNT embedded within a methylcellulose hydrogel. Finally, we used the NIR probe to measure SWCNT center wavelength and intensity from live immunocompetent mice. We anticipate that this framework may be broadly applicable to the development of near infrared nanosensors with the potential for more rapid clinical diagnostic translation.

bioengineering↗

bioRxiv2025

A unifying theory of receptive field heterogeneity predicts hippocampal spatial tuning

Neural populations exhibit receptive fields that vary in their sizes and shapes. Despite the prevalence of such tuning heterogeneity, we lack a unified theory of its computational benefits. Here, we present a framework that unifies and extends previous theories, finding that receptive field heterogeneity generally increases the information encoded in population activity. The information gain depends on heterogeneity in receptive field size, shape, and on the dimensionality of the encoded quantity. For populations encoding two-dimensional quantities, such as place cells encoding allocentric spatial position, our theory predicts that both size and shape receptive field heterogeneity are necessary to induce information gain, whereas size heterogeneity alone is insufficient. We thus turned to CA1 hippocampal activity to test our theoretical predictions--in particular, to measure shape heterogeneity, which has previously received little attention. To overcome limitations of traditional methods for estimating place cell tuning, we developed a fully probabilistic approach for measuring size and shape heterogeneity, in which tuning estimates were strategically weighted by explicitly measured uncertainty arising from biased or incomplete traversals of the environment. Our method furnished evidence that hippocampal receptive fields indeed exhibit strong degrees of size and shape heterogeneity, abiding by the normative predictions of our theory. Overall, our work makes novel predictions about the relative benefits of receptive field heterogeneities beyond our application to place cells, and provides a principled technique for testing them.

neuroscience↗

bioRxiv2025

Multiplexed Molecularly-Specific SWCNT Cytokine Sensing is Enabled and Enhanced by Amine-Functionalized DNA Aqueous Two-Phase Extraction

Single-walled carbon nanotubes (SWCNT) are versatile building blocks for optical sensors. Their carbon lattice structure, denoted by chiral (n,m) indices, determines the wavelength of their near infrared fluorescence emission. Commercially available SWCNT are polydisperse, containing many (n,m) species with overlapping fluorescence peaks, leading to spectral congestion. Aqueous two-phase extraction (ATPE) has emerged as a valuable tool to sort SWCNT according to their chiral structure and promises improved nanosensor performance as well as unique opportunities for multiplexing. Previous applications of ATPE have enabled ratiometric sensors, but simultaneous detection of multiple analytes using distinct SWCNT chiralities has yet to be realized. This hurdle is largely due to the contrasting surface chemistries needed for ATPE sorting and those needed for biosensing applications. In this work, we introduce a method for purifying multiple chiralities of SWCNT which can be functionalized with molecularly specific antibodies to create a multiplexed sensor platform. We utilize aminated DNA (DNA-NH2), which serves as both a conduit for ATPE sorting and an intermediate linker for later antibody conjugation, to realize this goal. We demonstrate that chemically modified DNA enables ATPE sorting, supporting the broad future utility of the technique. We further show that chirality-sorted SWCNT-DNA-NH2 serve as excellent base constructs for antibody conjugation, forming highly specific sensors with enhanced optical properties (compared to unsorted SWCNT sensors). Importantly, one batch of monochiral base constructs can be functionalized with different antibodies towards different biological applications. Finally, we show that we can sort and functionalize two separate SWCNT chiralities and combine them together to create a multiplexed sensor platform where each peak responds distinctly to its target. There are vast opportunities for expansion of this work through the use of alternative DNA sequences to enable sorting, various SWCNT chiralities, and virtually any commercially available antibody for molecular recognition.

bioengineering↗

bioRxiv2025

Diet links gut chemistry with cancer risk in C57Bl/6 mice and human colorectal cancer patients

The gastrointestinal tract is a complex ecosystem in which host tissues, microbial communities, and dietary inputs interact to shape metabolic outputs and epithelial homeostasis. Western-style diets, characterized by high fat and protein and low micronutrient content, represent a sustained ecological perturbation linked to colorectal cancer (CRC), yet specific mechanisms that impact risk remain poorly defined. Here, using a purified Western-style diet (NWD1) that induces sporadic intestinal and colon tumors in wild-type C57BL/6 mice we demonstrate how chronic dietary disturbance restructures gut community composition and sulfur metabolism, and how these changes modulate intestinal stem cell responses. Mice fed NWD1 for 24 weeks exhibited consistent shifts in ecosystem function, including a threefold increase in fecal sulfide production (P < 0.00001) and expansion of Erysipelotrichaceae family taxa. This altered chemical landscape associates with increased expression of mitochondrial sulfide oxidation pathways in Lgr5hi intestinal stem cells. Meta-analysis of human CRC cohorts revealed concordant enrichment of Erysipelotrichaceae, alongside established CRC-associated taxa such as Solobacterium moorei, indicating conserved ecological signatures across systems. Together, these findings support a model in which the Western-style diet drives a persistent shift in gut ecosystem structure and function toward a sulfide producing state that challenges epithelial homeostasis, with conserved microbial and metabolic configurations emerging prior to overt disease in mouse models.

cancer biology↗

bioRxiv2024

Characterization of alternative mRNA splicing associated with tumor thrombus in clear-cell renal cell carcinoma

Approximately 10% of Renal Cell Carcinoma (RCC) tumors form a thrombus that invades into nearby vasculature and is associated with lower survivability. However, the mechanisms driving tumor thrombus formation and progression are poorly understood. We therefore examined a publicly available RNAseq dataset of samples from RCC patients containing tumors with and without thrombi, as well as associated normal kidney tissues, and compared them. Using cell deconvolution, we found indications that kidney tumors and thrombi are associated with loss of mature kidney-specific cell types and gain of proliferative characteristics. Moreover, we identified a set of transcripts that are alternatively spliced between tumors with and without thrombi. Using motif enrichment analysis for known RNA binding proteins we found putative splicing regulators that are presumably associated with thrombus formation. We believe that this study will assist in unraveling cellular and molecular mechanisms of tumor progression and thrombus formation in RCC.

cancer biology↗

bioRxiv2024

SLAYER: Synthetic Lethality Analysis for Enhanced Targeted Therapy Implicates AhR inhibitor as a Target in RB1-Mutant Bladder Tumors

Synthetic lethality represents a promising therapeutic approach in precision oncology, yet systematic identification of clinically relevant synthetic lethal interactions remains challenging. Here we present SLAYER (Synthetic Lethality AnalYsis for Enhanced taRgeted therapy), a computational framework that integrates cancer genomic data and genome-wide CRISPR knockout screens to identify potential synthetic lethal interactions. SLAYER employs parallel analytical approaches examining both direct mutation-dependency associations and pathway-mediated relationships across 808 cancer cell lines. Our integrative method identified 4,332 statistically significant interactions, which were refined to 142 high-confidence candidates through stringent filtering for effect size, druggability, and clinical prevalence. Systematic validation against protein interaction databases revealed a 15-fold enrichment of known associations among SLAYER predictions compared to random gene pairs. Through pathway-level analysis, we identified inhibition of the aryl hydrocarbon receptor (AhR) as potentially synthetically lethal with RB1 mutations in bladder cancer. Experimental studies demonstrated selective sensitivity to AhR inhibition in RB1-mutant versus wild-type bladder cancer cells, which probably operates through indirect pathway-mediated mechanisms rather than direct genetic interaction. In summary, by integrating mutation profiles, gene dependencies, and pathway relationships, our approach provides a resource for investigating genetic vulnerabilities across cancer types.

bioinformatics↗

bioRxiv2023

Noninvasive Injectable Optical Nanosensor-Hydrogel Hybrids Detect Doxorubicin in Living Mice

While the tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNTs) imparts substantial potential for use in non-invasive biosensors, development of non-invasive systems is yet to be realized. Here, we investigated the functionality of a SWCNT-based nanosensor in several injectable SWCNT-hydrogel systems, ultimately finding SWCNT encapsulation in a sulfonated methylcellulose hydrogel optimal for detection of ions, small molecules, and proteins. We found that the hydrogel system and nanosensor signal were stable for several weeks in live mice. We then found that this system successfully detects local injections of the chemotherapeutic agent doxorubicin in mice. We anticipate future studies to adapt this device for detection of other analytes in animals and, ultimately, patients.

bioengineering↗

bioRxiv2023

Optical nanosensor passivation enables highly sensitive detection of the inflammatory cytokine IL-6

Interleukin-6 (IL-6) is known to a play critical role in the progression of inflammatory diseases such as cardiovascular disease, cancer, sepsis, viral infection, neurological disease, and autoimmune diseases. Emerging diagnostic and prognostic tools, such as optical nanosensors, experience challenges in successful clinical application in part due to protein corona formation dampening their selectivity and sensitivity. To address this problem, we explored the rational screening of several classes of biomolecules to be employed as agents in non-covalent surface passivation as a strategy to screen interference from non-specific proteins. Findings from this screening were applied to the detection of IL-6 by a fluorescent antibody-conjugated single-walled carbon nanotube (SWCNT)-based nanosensor. The IL-6 nanosensor exhibited highly sensitive and specific detection after passivation with a polymer, poly-L-lysine, as demonstrated by IL-6 detection in human serum within a clinically relevant range of 25 pg/mL to 25,000 pg/mL, exhibiting a limit of detection over three orders of magnitude lower than prior antibody-conjugated SWCNT sensors. This work holds the potential for rapid and highly sensitive detection of IL-6 in clinical settings with future application to other cytokines or disease-specific biomarkers.

bioengineering↗

bioRxiv2020

Recurrent dynamics of prefrontal cortex during context-dependent decision-making

A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animals performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

neuroscience↗