Biology subjects

Forjaz, A.

Publications and source records attributed to Forjaz, A..

8 recordsLinked to original sources

bioRxiv2024

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 {micro}m, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

bioinformatics↗

bioRxiv2024

A TBX5-dependent compartment boundary patterns the cardiac interventricular septum

Failure of septation of the interventricular septum (IVS) is the most common congenital heart defect (CHD), but mechanisms for patterning the IVS are largely unknown. Here, we show that a Tbx5+/Mef2cAHF+ progenitor lineage forms a compartment boundary bisecting the IVS. This coordinated population originates at a first- and second heart field interface. Ablation of Tbx5+/Mef2cAHF+ progenitors cause IVS disorganization, right ventricular hypoplasia and mixing of IVS lineages. Reduced dosage of the CHD transcription factor TBX5 disrupts boundary position and integrity, resulting in ventricular septation defects (VSDs) and patterning defects, including misexpression of Slit2 and Ntn1, which encode guidance cues. Reducing NTN1 dosage partly rescues cardiac defects in Tbx5 mutant embryos. Loss of Slit2 or Ntn1 causes VSDs and perturbed septal lineage distributions. Thus, we identify Tbx5 as a candidate selector gene, directing progenitors and regulating essential cues, to pattern a compartment boundary for proper cardiac septation, revealing mechanisms for cardiac birth defects.

developmental biology↗

bioRxiv2023

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNF, IFN{gamma}, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.

immunology↗

bioRxiv2023

Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures

Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.

developmental biology↗

bioRxiv2023

Three-dimensional assessments are necessary to determine the true spatial tissue composition of diseased tissues

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter-and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.

cancer biology↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2023

Precision-engineered biomimetics: the human fallopian tube

The fallopian tube has an essential role in several physiological and pathological processes from pregnancy to ovarian cancer. However, there are no biologically relevant models to study its pathophysiology. The state-of-the-art organoid model has been compared to two-dimensional tissue sections and molecularly assessed providing only cursory analyses of the models accuracy. We developed a novel multi-compartment organoid model of the human fallopian tube that was meticulously tuned to reflect the compartmentalization and heterogeneity of the tissues composition. We validated this organoids molecular expression patterns, cilia-driven transport function, and structural accuracy through a highly iterative platform wherein organoids are compared to a three-dimensional, single-cell resolution reference map of a healthy, transplantation-quality human fallopian tube. This organoid model was precision-engineered to match the human microanatomy. One sentence summaryTunable organoid modeling and CODA architectural quantification in tandem help design a tissue-validated organoid model.

bioengineering↗

bioRxiv2023

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

cancer biology↗