Biology subjects

Pino, J. C.

Publications and source records attributed to Pino, J. C..

8 recordsLinked to original sources

bioRxiv2023

Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity at a cell-type-specific level to better understand and predict the function of complex biological systems, such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverages due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (Microdroplet Processing in One pot for Trace Samples), the multiplexed isobaric labelling, and a nanoflow peptide fractionation approach. The integrated workflow allowed to maximize proteome coverage of laser-isolated tissue samples containing nanogram proteins. We demonstrated the deep spatial proteomics can quantify more than 5,000 unique proteins from a small-sized human pancreatic tissue pixel ([~]60,000 {micro}m2) and reveal unique islet microenvironments.

biochemistry↗

bioRxiv2023

CDK4/6 inhibition enhances SHP2 inhibitor efficacy and is dependent upon restoration of RB function in malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNST) are highly aggressive soft tissue sarcomas with limited treatment options, and novel effective therapeutic strategies are desperately needed. We observe anti-proliferative efficacy of genetic depletion or pharmacological inhibition using the clinically available SHP2 inhibitor (SHP2i) TNO155. Our studies into the signaling response to SHP2i reveal that resistance to TNO155 is partially mediated by reduced RB function, and we therefore test the addition of a CDK4/6 inhibitor (CDK4/6i) to enhance RB activity and improve TNO155 efficacy. In combination, TNO155 attenuates the adaptive response to CDK4/6i, potentiates its anti-proliferative effects, and converges on enhancement of RB activity, with greater suppression of cell cycle and inhibitor-of-apoptosis proteins, leading to deeper and more durable anti-tumor activity in in vitro and in vivo patient-derived models of MPNST, relative to either single agent. Overall, our study provides timely evidence to support the clinical advancement of this combination strategy in patients with MPNST and other tumors driven by loss of NF1.

cancer biology↗

bioRxiv2022

Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller sample number and amount of tissue measu red, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of tissues involved while maintaining spatial information and the degree of the islets sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be measured. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

systems biology↗

bioRxiv2022

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1-MPNST), but can occur sporadically. Through a multi-institution collaboration, we have developed 13 NF1-associated MPNST patient-derived xenografts (PDX). Genomic analysis of the PDX-tumor pairs identified somatic mutations in NF1 (61%), SUZ12 (61%), EED (15%), and TP53 (15%), and chromosome 8 (Chr8) gain (77%), consistent with published data. Pre-clinical models that capture this molecular heterogeneity are needed to identify and prioritize effective drug candidates for clinical translation. Here, we describe the successful development of a medium-throughput ex vivo 3D microtissue model with several advantages over 2D cell line growth, which can be utilized to predict drug response in vivo. Herein, we present proof-of-principle of this PDX-to-microtissue system, using four genomically representative MPNST and three drugs. This work highlights the development of a novel ex vivo to in vivo preclinical platform in MPNST that successfully captures the genomic diversity observed in patients and represents a resource to identify future therapeutic strategies.

cancer biology↗

bioRxiv2021

Probability-based mechanisms in biological networks with parameter uncertainty

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python Dynamic analysis of biochemical NetwOrks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that Bayesian parameter optimization can yield thousands of parameter vectors with equally good fits to experimental data. Our analysis however enables us to identify the dynamic differences between these parameter sets and identify three dominant execution modes. We further demonstrate that parameter vectors from each execution mode exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells to identify its signal execution modes. Our analysis identifies a previously unrecognized mechanistic explanation for the survival responses of the CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses. Impact StatementGiven the mechanistic models of network-driven cellular processes and the associated parameter uncertainty, we present a framework that can identify dominant reaction paths that could in turn lead to unique signal execution modes (i.e., dominant paths of flux propagation), providing a novel statistical and mechanistic insights to explain and predict signal processing and execution.

systems biology↗

bioRxiv2020

A computational framework to explore cellular response mechanisms from multi-omics datasets

Recent technological advances have made it feasible to collect multi-condition transcriptome and proteome time-courses of cellular response to perturbation. The increasing size and complexity of these datasets impedes mechanism of action discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we introduce MAGINE, a software framework to explore complex time-course multi-omics datasets and build mechanistic hypotheses of dynamic cellular response. MAGINE combines data management, enrichment, and network analysis and visualization within an interactive, Jupyter notebook-based environment to enable human-in-the-loop inquiry of complex datasets. We demonstrate how measurements from HL-60 cellular response to bendamustine treatment can be used to build a mechanistic, multi-resolution description of cellular commitment to fate. We present a systems-level description of signal execution from cellular DNA-damage response, to cell cycle arrest, and eventual commitment to apoptosis, mediated by over 2000 biochemical species. We further show that MAGINE can reveal unexpected, non-canonical effects of bendamustine treatment, including disruption of cellular pathways relevant to HIV infection response. MAGINE is available from https://github.com/lolab-vu/magine.

systems biology↗

bioRxiv2020

Accelerated Simulations of Chemical Reaction Systems using the Stochastic Simulation Algorithm on GPUs

1Stochasticity due to fluctuations in chemical reactions can play important roles in cellular network-driven processes. Although the Stochastic Simulation Algorithm (SSA, aka Gillespie Algorithm) has long been accepted as a suitable method to solve the time-dependent chemical master equation, its computational cost is prohibitive for large scale complex networks such as those found in cellular processes. Here we present GPU-SSA, an implementation of the SSA formalism utilizing Graphics Processing Units for use in Python using the PySB modeling framework. We show that the GPU implementation of SSA can achieve significant speedup compared to parallel CPU or single-core CPU implementations. We further include supplementary didactic material to demonstrate how to incorporate GPU-SSA workflows for interested readers.

systems biology↗

bioRxiv2019

Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural sounds

O_LIAudio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature.\nC_LIO_LIWe developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chippers accuracy, repeatability, and reproducibility.\nC_LIO_LIChipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings.\nC_LIO_LIChipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.\nC_LI

animal behavior and cognition↗