Biology subjects

Giri, A.

Publications and source records attributed to Giri, A..

At least 19 recordsLinked to original sources

bioRxiv2025

Clinical development of gene edited tacrolimus-resistant Treg (FKBP12KO-Treg) to enable simultaneous immunosuppression and support of immune regulation

Background: Unwanted immune responses play a central role in the pathogenesis of solid organ allograft rejection. These are managed by life-long immunosuppression with considerable burden for the patient and society. Adoptive therapy with regulatory T-cells (Treg) is a promising approach to restore sustainable immune balance and avoid long-term adverse effects of immunosuppression. While Treg effectively inhibit activation of unwanted immune responses, they are less effective in controlling pre-existing/activated memory effector T-cells (Teff). Thus, co-administration of Treg with immunosuppressants is required to achieve a sustainable organ acceptance. Calcineurin inhibitors (CNI) are powerful in controlling de novo generated and preformed Teff. However, CNI also dampen Treg immunoregulatory function. Thus, we hypothesize improved results of adoptive Treg therapy in immunosuppressed patients applying tacrolimus-resistant Treg. Methods: While retaining CNI modulation of Teff with tacrolimus, we knocked-out FKBP12 in Treg (FKBP12KO-Treg) by gene-editing using ribonucleoprotein-based CRISPR/Cas9 technology to generate tacrolimus-resistant Treg and characterised them using flow cytometry, functional assays and in-depth phenotyping. Results: This detailed in vitro analysis showed FKBP12KO-Treg were comparable to non-gene edited Treg and impervious to tacrolimus while maintaining immunoregulatory function and sensitivity to alternative CNIs raising no safety concerns. Furthermore, we aligned our methodology to achieve GMP compliance laying the basis for a manufacturing license in preparation of a clinical trial. Conclusion: Based on the presented preclinical dataset implying safety and efficacy of FKBP12KO-Treg, we are now seeking to undertake a proof-of-concept clinical trial to evaluate the co-administrationof FKBP12KO-Treg and tacrolimus to enhance the management of living donor kidney transplant recipients.

immunology↗

bioRxiv2025

Identification of Freezing Tolerance QTLs in Tripsacum dactyloides Using Open-Pollinated Bulk Segregant Analysis

This study investigates the genetic basis of freezing tolerance in Tripsacum dactyloides and related subspecies as a potential source of valuable traits for improving maize agriculture. Recognizing the significant economic losses in corn yields due to frost damage, we hypothesized that northern populations of T. dactyloides are enriched for freezing tolerance alleles. 40 diverse Tripsacum accessions were collected from natural populations and long-established field collections and used to generate F1 hybrids and open-pollinated F2 families. F2 seedlings were germinated then screened within a growth chamber for freezing tolerance by exposure to freezing temperatures. Seedlings were then phenotyped by tissue survival, and extremes were pooled to create tolerant and susceptible bulks. DNA sequencing was performed on founders, F1s, and tolerant/susceptible F2 bulks. To overcome challenges in traditional SNP calling in bulked samples, we developed a regression-based approach to estimate gamete frequencies and impute allele frequencies in pooled populations. The results showed genetic diversity among Tripsacum accessions, with divergence between northern and southern populations. We tracked segregation of alleles across genomic loci, and performed a joint bulk segregant analysis, identifying 9 QTLs significantly associated with freezing tolerance. These findings highlight potential loci for freezing tolerance that could inform genetic engineering of maize. Central HypothesisNorthern populations of Tripsacum dactyloides, a wild relative of maize, are enriched for freezing tolerance alleles which can be identified by mapping.

genomics↗

bioRxiv2025

Localization of Realistic Spatial Patches of Complex Source Activity in MEG

Accurate localization of neural sources in Magnetoencephalography (MEG) and Electroencephalography (EEG) is essential for advancing clinical and research applications in neuroscience. Traditional approaches like dipole fitting (e.g., MUSIC, RAP-MUSIC) are limited to discrete focal sources, while distributed source imaging methods (e.g., MNE, sLORETA) assume sources distributed across the cortical surface. These methods, however, often fail to capture sources with complex spatial extents, limiting their accuracy in realistic settings. To address these limitations, we introduce PATCH-AP, an enhanced version of the Alternating Projection (AP) method that effectively localizes both discrete and spatially extended sources. We evaluated PATCH-AP against leading source localization methods, including distributed source imaging techniques (MNE, sLORETA), traditional dipole fitting (AP), and recent extended source methods (Convexity-Champagne (CC), FLEX-AP). PATCH-AP consistently outperformed these methods in simulations, achieving lower Earth Movers Distance (EMD) scores--a metric indicating closer alignment with the true source distribution. In tests with real MEG data from a face perception task, PATCH-AP demonstrated high alignment with the fusiform face area, a region critical for face processing. These results highlight PATCH-APs potential to enhance source localization accuracy, promising significant advancements in neuroscience research and clinical diagnostics.

neuroscience↗

bioRxiv2025

Spatiotemporal Dynamics of Invariant Face Representations in the Human Brain

The human brain can effortlessly extract a familiar faces age, gender, and identity despite dramatic changes in appearance, such as head orientation, lighting, or expression. Yet, the spatiotemporal dynamics underlying this ability, and how they depend on task demands, remain unclear. Here, we used multivariate decoding of magnetoencephalography (MEG) responses and source localization to characterize the emergence of invariant face representations. Human participants viewed natural images of highly familiar celebrities that systematically varied in viewpoint, gender, and age, while performing a one-back task on the identity or the image. Time-resolved decoding revealed that identity information emerged rapidly and became increasingly invariant to viewpoint over time. We observed a temporal hierarchy: view-specific identity information appeared at 64 ms, followed by mirror-invariant representations at 75 ms and fully view-invariant identity at 89 ms. Identity-invariant age and gender information emerged around the same time as view-invariant identity. Task demands modulated only late-stage identity and gender representations, suggesting that early face processing is predominantly feedforward. Source localization at peak decoding times showed consistent involvement of the occipital face area (OFA) and fusiform face area (FFA), with stronger identity and age signals than gender. Our findings reveal the spatiotemporal dynamics by which the brain extracts view-invariant identity from familiar faces, suggest that age and gender are processed in parallel, and show that task demands modulate later processing stages. Together, these results offer new constraints on computational models of face perception.

neuroscience↗

bioRxiv2025

A modular, adaptable, and accessible implant kit for chronic electrophysiological recordings in rats

Electrophysiological implants enable exploration of the relationship between neuronal activity and behavior. These technologies evolve rapidly, with multiple iterations of recording systems developed and utilized. Chronic implants must address a litany of complications, including retention of high signal-to-noise ratio in probes and the ability to withstand excess force over the experimental period. To overcome these issues, we designed a chronic implant for rats. Our comprehensive protocol optimizes the entire implant process, from assembling and testing the probes (Neuropixels) to implantation. In addition to addressing the complications previously mentioned, our implant can vertically adjust probes with micron precision and is constructed using modular components, allowing it to be easily modified for various research contexts, electrophysiological recording systems, headstages, and probe types.

neuroscience↗

bioRxiv2025

Epithelial-mesenchymal transition couples with cell cycle arrest at various stages

Numerous computational approaches have been developed to infer cell state transition trajectories from snapshot single-cell data. Most approaches first require projecting high-dimensional data onto a low-dimensional representation; however, this can distort the dynamics of the system. Using epithelial-to-mesenchymal transition (EMT) as a test system, we show that both biology-guided low-dimensional representations and trajectory simulations in high-dimensional state space, not representations obtained with brute force dimensionality-reduction methods, reveal two broad paths of TGF-{beta} induced EMT. The paths arise from the coupling between cell cycle and EMT at either the G1 or G2/M phase, contributing to cell-cycle related EMT heterogeneity. Subsequent multi-plex immunostaining studies confirmed the multiple predicted paths at the protein level. The present study highlights the heterogeneity of EMT paths, emphasizes that caution should be taken when inferring transition dynamics from snapshot single-cell data in two- or three-dimensional representations, and shows that incorporating dynamical information can improve prediction accuracy.

systems biology↗

bioRxiv2024

Computational Primitives for Cost-Benefit Decision-Making

Cost-benefit decision-making is a critical process performed by all organisms, including humans. Various factors, including risk1,2, uncertainty3, age4, sex5, and neuropsychiatric disorders6, can alter decision-making. To explore cost-benefit decision-making in humans, we developed a comprehensive task and analysis framework that presents participants with a series of approach-avoid trade-offs across a variety of contexts. With this system, we found that cost-benefit decisions in humans are made using a set of computational strategies that may be used for integrating costs and rewards, which we term decision-making primitives. We further show that these decision-making primitives are used by rodents performing a similar decision-making task7. We find that utilization of these primitives in both rodents and humans shifts based on factors like hunger and sex, and that individuals use primitives differently. We additionally demonstrate that using a naturally-inspired neural network architecture generates output that overlaps with human and rodent performance over a non-constrained neural network. This novel conceptual framework, by isolating discrete decision-making primitives, has potential to help us identify how different brain regions give rise to decision-making behavior, as well as to facilitate better diagnosis of neuropsychiatric disorders and development of naturally-inspired artificial intelligence systems of decision-making.

neuroscience↗

bioRxiv2024

Effect of Acute Alcohol Consumption in a Novel Rodent Model of Decision Making

BackgroundAlcohol use, especially at high consumption levels, can lead to irrational decision-making. In humans, this can lead to harmful outcomes often seen in the context of driving under the influence and or aggressive behavior. To date, the field is lacking comprehensive animal models to examine the impact of alcohol use on decision making in rodents, particularly to examine sex differences in choice behavior. To address this issue, the present study examined the effects of acute alcohol consumption during a behavioral approach-avoidance task that captures momentary changes in decision-making behavior and choice selection in female and male rats. MethodsOur team has developed a novel behavioral protocol involving a concurrent choice to consume four different concentrations of alcohol and sucrose combinations. During the task, female or male rats can approach or avoid drinking solutions in four distinct corners of our test apparatus. The solutions were prepared in inverse concentrations (higher sucrose was paired with lower alcohol and vice versa) so that the rodents pursue minimal alcohol use by consuming the higher sucrose concentrations or higher concentrations of alcohol by drinking the lower sucrose concentrations. The animals also have the option to avoid drinking alcohol by not approaching any of the drinking cups. Behavior and choice were tracked during task performance involving different solution concentrations of alcohol and sucrose. ResultsThe choice of consuming different concentrations of alcohol or sucrose resulted in sex-dependent differences in an approach-avoid trade-off pattern of behavior that was sensitive to different concentrations of alcohol/sucrose combinations. Notably, males were greatly affected by the introduction of alcohol into the task environment, approaching higher alcohol concentrations significantly more often than the non-alcohol containing options. In contrast, females choice patterns and task performance were largely unchanged during alcohol and non-alcohol containing tasks. Regardless of sex, we identify a novel method for identifying individual subject decision-making abnormalities during and after alcohol consumption. ConclusionsThis research reveals a novel approach for examining the effects of acute alcohol exposure during a trade-off task, with decision patterns being more impacted by alcohol use in males as compared to females. We also offer the field a novel approach for identifying individual abnormalities in decision making behavior with the presentation of alcohol. Future research can explore these abnormal patterns in both acute and chronic alcohol conditions to develop methods for identifying subjects at-risk for developing an alcohol use disorder and the deleterious impact of alcohol on rational decision making.

neuroscience↗

bioRxiv2024

Model of a striatal circuit exploring biological mechanisms underlying decision-making during normal and disordered states

Decision-making requires continuous adaptation to internal and external contexts. Changes in decision-making are reliable transdiagnostic symptoms of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a mathematical space for decision-making computations depending on context, and how the matrix compartment defines action value depending on the space. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model reframes the role of the striosomal circuit in neuroeconomic and disorder-affected decision-making. HighlightsO_LIStriosomes prioritize decision-related data used by matrix to set action values. C_LIO_LIStriosomes and matrix have different roles in the direct and indirect pathways. C_LIO_LIAbnormal information organization/valuation alters disorder presentation. C_LIO_LIVariance in data prioritization may explain individual differences in disorders. C_LI eTOCBeck et al. developed a computational model of how a striatal circuit functions during decision-making. The model unifies and extends theories about the direct versus indirect pathways. It further suggests how aberrant circuit function underlies decision-making phenomena observed in neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Localization of Spatially Extended Brain Sources by Flexible Alternating Projections (FLEX-AP)

Magnetoencephalography (MEG) and electroencephalography (EEG) are widely employed techniques for the in-vivo measurement of neural activity with exceptional temporal resolution. Modeling the neural sources underlying these signals is of high interest for both neuroscience research and pathology. The method of Alternating Projection (AP) was recently shown to outperform the well-established recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this work, we further enhanced AP to allow for source extent estimation, a novel approach termed flexible extent AP (FLEX-AP). We found that FLEX-AP achieves significantly lower errors for spatially coherent sources compared to AP, RAP-MUSIC, and the corresponding extension, FLEX-RAP-MUSIC. We also found an advantage for discrete dipoles under forward modeling errors encountered in real-world scenarios. Together, our results indicate that the FLEX-AP method can unify dipole fitting and distributed source imaging into a single algorithm with promising accuracy.

neuroscience↗

bioRxiv2023

Interlinked Bi-stable Switches Govern the Cell Fate Commitment of Embryonic Stem Cells

Cell fate decision-making events of embryonic stem (ES) cells are often governed by bi-stable switch-like steady state dynamics of two key transcription factors Oct4 and Nanog due to mutual positive feedback regulation. Intriguingly, for the differentiation of ES cells to either extraembryonic trophectoderm (TE) and primitive endoderm (PrE) lineages, Oct4 maintains either low (TE) or high PrE) expression levels relative to a moderate level in ES cells, while Nanog exhibits high expression in ES state and low expression in both the differentiated states. The dynamical origin for such kind of disparate steady-state level maintenance of Oct4 and Nanog remains elusive. Herein, we demonstrate that such complex steady-state dynamics can be hypothesized and explained by two different bistable switches interconnected either in a stepwise (Oct4) or in a mushroom-like (Nanog) manner. Our hypothesis qualitatively reconciles various experimental observations and elucidates how different feedback and feed-forward motifs orchestrate the extraembryonic development and stemness maintenance of ES cells. Importantly, the model predicts strategies to optimize the dynamics of self-renewal and differentiation of embryonic stem cells that may have therapeutic relevance in the future.

systems biology↗

bioRxiv2022

Profiling emergent behaviors from single-cell motility

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, we introduce CaMI, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n=74,253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions, and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, we present a multivariate framework to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations. TeaserA computational framework to identify and classify single-cell motility patterns and phenotypic heterogeneity across biological conditions.

bioengineering↗

bioRxiv2022

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy - when a single gene controls two or more seemingly unrelated traits - has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of true biological pleiotropy across all maize phenotypes is largely unknown. Here we investigate the extent to which biological pleiotropy impacts phenotypes within maize through GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assessed the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide- and large-effect variants, leaving the prevalence of detectable pleiotropy relatively low. Author SummaryThe genetic basis of complex traits has been thought to exhibit pleiotropy, which is the notion that a single locus can control two or more unrelated traits. Widespread reports in the human disease literature show genomic signatures of pleiotropic loci across many traits. However, little is known about the prevalence and behavior of pleiotropy in maize across a large number of phenotypes. Using association mapping of common alleles in over one hundred thousand traits, we determine how pleiotropic each region was and use these quantitative scores to functionally characterize each region of the genome. Our results show little evidence that pleiotropy is a common phenomenon in maize. We observed that maize does not exhibit the same pleiotropic characteristics as human diseases in terms of prevalence, gene expression, chromatin accessibility, or sequence conservation. Rather than pervasive pleiotropy, we hypothesize that strong selection on large and wide effect loci and the need for trait independence at the gene level keep the prevalence of pleiotropy low, thus, allowing for the adaptation of maize varieties to novel environments and conditions.

plant biology↗

bioRxiv2021

Network analysis after epigenome-wide methylation study revealed JUP as a regulator of co-methylated risk-module for T2DM

Controlling the global Type 2 diabetes mellitus (T2DM) pandemic requires a better understanding of its risk factors across different populations, and needs markers that can precisely predict the individual risk to the disease. DNA methylation due to environmental factors is a known mechanism for conferring risk to T2DM. To identify such methylation signatures and associated risk to the disease, we performed an epigenome-wide-association study (EWAS) in 844 individuals of Indo-European origin. Within the Indian population, we identified and validated 6 novel differentially methylated CpG sites in PDCD6IP, MIR1287, 5S_rRNA, HDAC9, KCNK16, and RTN1 genes associated with T2DM risk at the epigenome-wide-significance-level (p<1.2x10-7). Further, we also replicated the association of 3 known CpG sites in TXNIP, SREBF1 and CPT1A in the Indian population. Using methylation-based-network analysis, we identified 4 co-methylated modules, which we named as turquoise, yellow, brown, and blue, among differentially methylated CpG sites in discovery phase samples. We observed that methylation of the brown module with 28 CpG sites, associated with T2DM risk factors (e.g., BMI, insulin, C-peptide). Upon further analysis, we noted that these methylation signatures at 14 of the brown modules CpG can be used as marker to segregate T2DM patients with good glycemic control (e.g., low HbA1c) but poor lipid profile (low HDL and high TG) from the other patients. Additionally, we discovered that rs6503650 in the JUP gene regulates methylation at all the 14 hub CpG sites of the brown modules as methQTL. Our network-assisted epigenome-wide association study is the first to systematically explore DNA methylations conferring risks to T2DM. In addition, the study shows the potential use of identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patient in order to improve the clinical management and treatment outcomes.

genomics↗

bioRxiv2021

Haplotype Associated RNA Expression (HARE) Improves Prediction of Complex Traits in Maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels - a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) - for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Author summaryThe increasing availability of genomic data has been widely used in the prediction of many traits. However, genome wide prediction has been mostly carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable than overall measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence are highly transferable.

genetics↗

bioRxiv2021

Incoherent modulation of bi-stable dynamics orchestrates the Mushroom and Isola bifurcations

In biological networks, steady state dynamics of cell-fate regulatory genes often exhibit Mushroom and Isola kind of bifurcations. How these complex bifurcations emerge for these complex networks, and what are the minimal network structures that can generate these bifurcations, remain elusive. Herein, by employing Waddingtons landscape theory and bifurcation analysis, we have shown that both Mushroom and Isola bifurcations can be realized with four minimal network motifs that are constituted by combining positive feedback motifs with different types of incoherent feedback motifs. Our study demonstrates that the intrinsic bi-stable dynamics due to the presence of the positive feedback motif can be fine-tuned by altering the extent of the incoherence of these proposed minimal networks to orchestrate these complex bifurcations. These modeling insights will be useful in identifying and analyzing possible network motifs that may give rise to either Mushroom or Isola bifurcation in other biological systems.

systems biology↗

bioRxiv2020

Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer mortality by 2030. Bulk transcriptomic analyses have distinguished classical pancreatic tumors from basal-like tumors with more aggressive clinical behaviour. We derived PDAC organoids from primary tumors of 18 patients, together with two matched samples from liver metastases. By single-cell RNA sequencing, we show that PDAC organoids consist of ductal cells with patient-specific expression of several gene groups, including genes which encode cell surface proteins. We report classical and basal-like cells coexisting within single primary tumors or metastases, with greater intratumor subtype heterogeneity linked to higher tumor grade. Single-cell transcriptome analysis of PDAC organoids and primary PDAC identified distinct tumor cell states shared across patients, including a cycling progenitor cell state and a differentiated secretory state. We show that these cell states are connected by a differentiation hierarchy, with classical subtype cells concentrated at the endpoint of this hierarchy. In an imaging-based drug screen, expression of classical subtype genes also correlates with better response to clinical drugs. Our results thus uncover a functional hierarchy of PDAC cell states linked to transcriptional tumor subtypes, and support the use of PDAC organoids as a clinically relevant model for in vitro studies of tumor heterogeneity.

cancer biology↗

bioRxiv2019

The role of integrin αv and CD44 in GBM migration in human brain

Glioblastoma (GBM) is the most common primary adult malignant brain tumor. Recurrence is driven invading tumor cells that escape surgical resection and demonstrate resistance to standard-of-care chemotherapy and radiotherapy. A large body of research has been conducted on tumor cell motility. However, typical in vitro models make use of polystyrene culture dishes, which exhibit significantly different physical parameters than brain tissue. Here we report on the use of human organotypic brain slices as an ex vivo approach for the dynamic study of GBM cell motility. Temporal lobectomy tissue from epilepsy patients was obtained and cut into 350{micro}m thick slices. After the tissue slices had a weeks incubation for recovery, fluorescently labeled tumor cells were seeded. We then tracked individual tumor cells using time-lapse fluorescent confocal microscopy. Quantification of motility characteristics, including mean squared displacement, total path length, and consistency, allowed for comparison of different conditions, including knockdown of cell surface proteins integrin v (ITGAV) and CD44. Human organotypics demonstrated minimal variability across specimen in terms of motility parameters, including total path length, averaged instantaneous velocity, and consistency. Knockdown of the traditional motility protein ITGAV showed little effect on overall motility while knockdown of CD44 resulted in a significant reduction in both averaged instantaneous velocity and total path length. When the same parameters were examined using Matrigel, ITGAV and CD44 both showed decreased motility, highlighting the impact of the physical environment on cell behavior. Finally, cell motility in mouse organotypic slices was decreased when compared to human organotypic slices. Here we demonstrate the use of human organotypic brain slices in the study of GBM cell invasion. This model system offers a physiologically-relevant environment in which to examine the dynamic process of cell motility.

cancer biology↗