Biology subjects

Goda, K.

Publications and source records attributed to Goda, K..

16 recordsLinked to original sources

bioRxiv2026

Super-resolution imaging flow cytometry

Organelle architecture is a key determinant of cellular state. However, its heterogeneity remains difficult to quantify at population scale because conventional imaging flow cytometry is diffraction-limited. Here we present super-resolution imaging flow cytometry (SR-IFC), a high-throughput structured-illumination platform combining stroboscopic excitation, microfluidic hydrodynamic focusing and AI-assisted reconstruction. From three phase-shifted frames acquired along the flow axis, SR-IFC reconstructs images with approximately 118-nm isotropic in-plane resolution, about two-fold beyond the diffraction limit, at up to approximately 720 cells per second. In microbiology, SR-IFC resolves individual lipid droplets in nitrogen-starved Euglena gracilis and reveals spatial reorganization during lipid biogenesis missed by diffraction-limited imaging. In immunology, it maps activation-associated mitochondrial remodeling across primary human CD8+ T-cell differentiation, including a transient networked state. SR-IFC thus provides a broadly applicable platform for population-scale super-resolution phenotyping across diverse areas of biology.

bioengineering↗

bioRxiv2026

Image-scanning light-sheet microscopy for high-speed volumetric imaging of complex biological dynamics

Volumetric fluorescence microscopy is a powerful method for studying complex biological systems because it enables comprehensive observation of structural and physiological dynamics. In particular, light-sheet microscopy (LSM) is a leading option for real-time volumetric fluorescence imaging as it combines high imaging speed, low phototoxicity, minimal photobleaching, high spatiotemporal resolution, and low computational burden. To capture fast biological events, various efforts have been made to improve the imaging speed of volumetric fluorescence microscopy, including LSM. However, existing approaches entail significant trade-offs that make routine volumetric imaging at and beyond video rates challenging under practical conditions. Here, we introduce image-scanning LSM, a method that substantially increases the volumetric imaging speed achievable with LSM while preserving key performance metrics, such as spatial resolution and photon efficiency, as well as accessibility. Our implementation, termed image-scanning oblique plane (ISOP) microscopy, enables volumetric fluorescence imaging at up to 1,000 volumes per second with submicrometer lateral spatial resolution. We demonstrate the broad utility of ISOP microscopy by recording and analyzing the dynamics of behaving and rapidly moving organisms.

bioengineering↗

bioRxiv2025

ALKBH1 drives codon-biased, pro-oncogenic translation and tumor microenvironment remodeling in glioma via tRNA wobble oxidation

Gliomas rely on translational plasticity to sustain heterogeneity, stemness, and immune evasion. Here we identify the tRNA dioxygenase ALKBH1 as a central regulator of codon-biased translation and tumor microenvironment remodeling. LC-MS/MS profiling of patient tumors revealed enrichment of ALKBH1-mediated wobble cytidine oxidation modifications in high-grade gliomas. Genetic perturbation demonstrated that ALKBH1 overexpression slows proliferation in vitro but worsens survival in vivo by promoting glioma stem-like and neuronal states and suppressing anti-tumor immunity. Ribosome profiling showed that ALKBH1 establishes an A/T-ending codon-biased translational program, enhancing decoding of rare leucine codons (TTA/TTG) and driving synthesis of pro-stemness transcripts. Single-cell RNA-seq analyses further revealed that ALKBH1 promotes glioma heterogeneity and induces neuronal cell clusters and rewires intercellular communication ECM-related signaling while dampening immune pathways and reducing immune infiltration. These findings establish a mechanistic link between tRNA oxidation, codon bias, and glioma aggressiveness, positioning ALKBH1 as a potential therapeutic target for glioma.

cancer biology↗

bioRxiv2025

Direct, high-throughput linking of single cell imaging and gene expression

Understanding sequence-phenotype relationships in biology requires large-scale datasets that provide one-to-one mapping of sequence to phenotype. Here, we address this challenge by introducing multimodal droplet barcoding, enabling individual microfluidic droplets to be tracked across both optical and sequencing measurements. We demonstrate the power of this approach through multimodal single-cell analysis. We pair imaging with gene expression profiling of individual cells, identifying genes that correlate with phenotypic features.

genomics↗

bioRxiv2025

High-throughput single cell -omics using semi-permeable capsules

Biological systems are inherently complex and heterogeneous. Deciphering this complexity increasingly relies on high-throughput analytical methods and tools that efficiently probe the cellular phenotype and genotype. While recent advancements have enabled various single-cell -omics assays, their broader applications are inherently limited by the challenge of efficiently conducting multi-step biochemical assays while retaining various biological analytes. Extending on our previous work (1) here we present a versatile technology based on semi-permeable capsules (SPCs), tailored for a variety of high-throughput nucleic acid assays, including digital PCR, genome sequencing, single-cell RNA-sequencing (scRNA-Seq) and FACS-based isolation of individual transcriptomes based on nucleic acid marker of interest. Being biocompatible, the SPCs support single-cell cultivation and clonal expansion over long periods of time - a fundamental limitation of droplet microfluidics systems. Using SPCs we perform scRNA-Seq on white blood cells from patients with hematopoietic disorders and demonstrate that capsule-based sequencing approach (CapSeq) offers superior transcript capture, even for the most challenging cell types. By applying CapSeq on acute myeloid leukemia (AML) samples, we uncover notable changes in transcriptomes of mature granulocytes and monocytes associated with blast and progenitor cell phenotypes. Accurate representation of the entirety of the cellular heterogeneity of clinical samples, driving new insights into the malfunctioning of the innate immune system, and ability to clonally expand individual cells over long periods of time, positions SPC technology as customizable, highly sensitive and broadly applicable tool for easy-to-use, scalable single-cell -omics applications.

genomics↗

bioRxiv2024

Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables. Author summaryMany traits of medical relevance are "complex" in that they are affected by both genetic and environmental factors. Thus, using genetic and environmental information in statistical methods has the potential to increase the accuracy of phenotypic prediction, the ultimate goal of precision medicine. However, the correlation between the genetic and environmental components (that arises when environmental variables are themselves genetically determined) and the correlations between environmental measures can be problematic for most statistical methods used for modeling complex traits. In this work, we investigated these issues using 27 lifestyle measures in addition to genetic information for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals. We show that including lifestyle and genetic data resulted in more accurate predictions than either data type alone. Moreover, adjusting the lifestyle measures for the genetic and other non-genetic effects can help improve the predictions further.

genetics↗

bioRxiv2024

Oligodendrocyte dependent synchronized activity orchestrates circuit maturation and brain functionalization

Synchronized spontaneous neural activity is a fundamental feature of developing central nervous systems and is thought to be essential for proper brain development. However, the mechanisms that regulate this synchronization and its long-term impact on brain function remain unclear. Here, we identify a previously unrecognized role of oligodendrocytes in orchestrating synchronized spontaneous activity during a critical developmental window, with lasting consequences for adult behavior. Using oligodendrocyte-specific genetic manipulation in the mouse cerebellum, we demonstrate that oligodendrocyte deficiency during early postnatal development, but not after weaning, disrupts the synchronization of Purkinje cell activity both during development and in adulthood. The early disruption produced persistent deficits in cerebellar-dependent behaviors, including anxiety, sociality, and motor function. Optogenetic re-synchronization in adulthood restored motor and social functions but not anxiety-like behavior, demonstrating that reduced Purkinje cell synchrony specifically drives the motor and social impairments. Our findings establish a causal link between developmental oligodendrocyte-regulated neural synchrony and the emergence of complex brain functions, which depend on the proper developmental trajectory necessary for driving brain function.

neuroscience↗

bioRxiv2024

Super homotypic targeting by exosome surface engineering

Homotypic targeting is the inherent ability of cells for preferential interaction with cells of similar or identical types, a phenomenon commonly seen in cell adhesion, tissue formation, and immune responses. Unfortunately, its full potential remains largely untapped. Here we introduce an approach to drastically boost the homotypic targeting capabilities of cells via exosomes (nanoscale extracellular vesicles secreted by cells). By engineering exosome surfaces with lanthanides, we amplify specific cell-exosome interactions by more than 25-fold, significantly accelerating the selective capture of exosomes by cells of the same lineage. This substantial enhancement in cellular homophilicity opens up an entirely new class of applications, two of which we showcase here with unprecedented performance: using cells to detect specific exosomes and using exosomes to detect specific cells. The concept of "super homotypic targeting" offers enormous potential to transform cancer diagnostics, immunotherapy, targeted drug delivery, tissue engineering, and vaccine development.

cell biology↗

bioRxiv2024

Flow zoometry of Drosophila

Drosophila serves as a highly valuable model organism across numerous fields including genetics, immunology, neuroscience, cancer biology, and developmental biology. Central to Drosophila-based biological research is the ability to perform comprehensive genetic or chemical screens. However, this research is often limited by its dependence on laborious manual handling and analysis, making it prone to human error and difficult to discern statistically significant or rare events amid the noise of individual variations resulting from genetic and environmental factors. In this article we present flow zoometry, a whole-animal equivalent of flow cytometry for large-scale, individual-level, high-content screening of Drosophila. Our flow zoometer automatically clears the tissues of Drosophila melanogaster, captures three-dimensional (3D) multi-color fluorescence tomograms of single flies with single-cell volumetric resolution at an unprecedented throughput of over 1,000 animals within 48 hours (24 hr for clearing; 24 hr for imaging), and performs AI-enhanced data-driven analysis - a task that would traditionally take months or years with manual techniques. To demonstrate its broad applications, we employed the flow zoometer in various laborious screening assays, including those in toxicology, genotyping, and tumor screening. Flow zoometry represents a pivotal evolution in high-throughput screening technology: previously from molecules to cells, now from cells to whole animals. This advancement serves as a foundational platform for "statistical spatial biology", to improve empirical precision and enable serendipitous discoveries across various fields of biology.

bioengineering↗

bioRxiv2023

inDrops-2: a flexible, versatile and cost-efficient droplet microfluidics approach for high-throughput scRNA-seq of fresh and preserved clinical samples

The development of a large variety of single-cell analytical techniques has empowered researchers to explore diverse biological questions at the level of individual cells. Among these, droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly prevalent owing to their high-throughput capabilities and reduced reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, the relatively high cost impose limitations for profiling large numbers of samples. Moreover, as the scope and scale of single cell sequencing methods keeps expanding, the possibility to accommodate diverse molecular biology workflows and inexpensively profile multiple biospecimens simultaneously becomes highly relevant. Herein, we present inDrops-2: an open-source scRNA-seq platform designed to profile fresh or preserved clinical samples with a sensitivity matching that of state-of-the-art commercial systems, yet at a few folds lower cost. Using inDrops-2, we conducted a comparative analysis of two prominent scRNA-seq protocols - those based on exponential and linear amplification of cDNA - and provide useful insights about the pros and cons inherited to each approach. We showcase the utility of inDrops-2 by simultaneously profiling 18 human lung carcinoma samples, all in one run, following cell preservation, long-term storage and multiplexing, to obtain a multiregional cellular profile of tumor microenvironment. The scalability, experimental flexibility and cost-efficiency offered by inDrops-2 should make it appealing for various single-cell transcriptomic studies.

molecular biology↗

bioRxiv2022

Nucleotide binding is the critical regulator of ABCG2 conformational transitions

ABCG2 is an exporter type ABC protein that can expel numerous chemically unrelated xeno- and endobiotics from cells. When expressed in tumor cells or tumor stem cells, ABCG2 confers multidrug resistance, contributing to the failure of chemotherapy. Molecular details orchestrating substrate translocation and ATP hydrolysis remain elusive. Here we present methods to concomitantly investigate substrate and nucleotide binding by ABCG2 in cells. Using the conformation-sensitive antibody 5D3, we show that the switch from the inward-facing (IF) to the outward-facing (OF) conformation of ABCG2 is induced by nucleotide binding. IF-OF transition is facilitated by substrates, and hindered by the inhibitor Ko143. Direct measurements of 5D3 and substrate binding to ABCG2 indicate that the high-to-low affinity switch of the drug binding site coincides with the transition from the IF to the OF conformation. Low substrate binding persists in the post-hydrolysis state, supporting that dissociation of the ATP hydrolysis products is required to reset the high substrate affinity IF conformation of ABCG2.

biochemistry↗

bioRxiv2022

Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm

A newly developed software, AgMate, was used to perform optimized mating for monoecious Pinus taeda L. breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding. Study implicationsA dataset from a breeding population of loblolly pine (Pinus taeda L.) was analyzed using an optimal mating software, AgMate (developed by the authors), to optimize the selection, contribution, and mating of candidates simultaneously. The software helps breeders make decisions on which tree to cross with which tree and how many times, such that the trees are not related to each other and would result in the best performing progenies. AgMate is effective in meeting the breeding objectives for monoecious species. The open-source, easy-to-use, and flexible AgMate software, also available as a website, is invaluable in helping breeders to create optimal matings for future generations, which balance the pursuit of maximizing genetic gain while maintaining genetic diversity.

plant biology↗

bioRxiv2022

Quantitative live-cell imaging of secretion activity reveals dynamic immune responses

The measurement of cytokine secretions has contributed to the development of immunology; however, new methods that enable highly sensitive and efficient analysis are required for the precise characterisation of dynamic secretion activity when using rare cells or limited human specimens. Here, we report a new technology for quantitative live-cell imaging of secretion activity (qLCI-S), that enables high-throughput and dual-colour detection of prolonged secretion activity at the single-cell level, followed by transcriptome analysis for individual cells based on their phenotype. The power of the qLCI-S was demonstrated by visualising the individual and longitudinal cytokine secretion patterns of group 2 innate lymphoid cells, which comprised <0.01% human peripheral blood mononuclear cells, and identifying their minor subpopulations. This new technology will provide new insights into the spatiotemporal dynamic nature of various secretory functions and the development of fundamental tools for phenotypic drug discovery and regenerative and precision medicine.

biophysics↗

bioRxiv2021

High-throughput Raman-activated cell sorting in the fingerprint region

Cell sorting is the workhorse of biological research and medicine. Cell sorters are commonly used to sort heterogeneous cell populations based on their intrinsic features. Raman-activated cell sorting (RACS) has recently received considerable interest by virtue of its ability to discriminate cells by their intracellular chemical content, in a label-free manner. However, broad deployment of RACS beyond lab-based demonstrations is hindered by a fundamental trade-off between throughput and measurement bandwidth (i.e., cellular information content). Here we overcome this trade-off and demonstrate broadband RACS in the fingerprint region (300 - 1,600 cm-1) with a record high throughput of ~50 cells per second. This represents a 100x throughput increase compared to previous demonstrations of broadband fingerprint-region RACS. To show the utility of our RACS, we demonstrate real-time label-free sorting of microalgal cells based on their accumulation of carotenoids and polysaccharide granules. These results hold promise for medical, biofuel, and bioplastic applications.

bioengineering↗

bioRxiv2020

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. For example, at high flow speed (i.e., high throughput), the integration time of the image sensor becomes short, resulting in reduced sensitivity or pixel resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually "freezing" the motion of flowing cells on the image sensor to effectively achieve 1,000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells/s without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

cell biology↗

bioRxiv2019

Classification of platelet aggregates by agonist type

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

cell biology↗