Biology subjects

Ma, T.

Publications and source records attributed to Ma, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Internal desynchrony of the circadian clock system in middle-aged mice under social jet lag-like conditions

Social jet lag (SJL) refers to the discrepancy in sleep patterns between weekdays and weekends, leading to a misalignment between the internal clock and social time. In this study, we investigated the effects of weekly shifts in light-dark (LD) conditions: two days per week with 6-h delayed LD cycles (simulating Saturday and Sunday), followed by a 6-h advance on Monday. Core body temperature rhythms rapidly entrained to the delayed LD cycles on weekends, and these delayed rhythms persisted even after the LD cycle was advanced on Monday. In contrast, plasma corticosterone rhythms on Mondays were aligned with the LD cycle but exhibited reduced amplitude. In the livers of SJL mice on Monday, the expression rhythms of Per1, Per2, and Hsp70 were delayed by 3-5 h compared to that in the controls, whereas Rev-erb expression rhythms remained comparable to those of the controls. The expression of lipid and glucose metabolism-related genes in the liver showed either delayed rhythms or no significant changes. To determine whether the dissociation of gene expression rhythms resulted from gene-specific responses to circadian body temperature and hormonal signals, we conducted ex vivo culture experiments using mouse liver slices. High-temperature stimulation induced Per2 and Hsp70 expression, while dexamethasone induced Per1 expression. High temperature and dexamethasone affected distinct sets of metabolic genes, whereas insulin induced only minor changes. Moreover, these responses were strongly influenced by the age and light exposure of the mice. We also examined the effect of weekly housing by providing environmental enrichment (EE), which had minimal impact on circadian parameters but promoted anti-aging effects on bone density and behavior. Overall, our findings indicate that weekly shifts in LD cycles induce internal desynchronization within the hepatic molecular clock and metabolic pathways by uncoupling core body temperature rhythms, hormonal rhythms, and gene-specific responses to stimuli.

physiology↗

bioRxiv2025

Association of cytochrome c oxidase dysfunction with amyloidosis in Alzheimer's disease and patient-derived cerebral organoids

Patients with Alzheimers disease (AD) demonstrate brain mitochondrial dysfunction and energy deficiency that are closely associated with cognitive impairment. Cytochrome c oxidase (CCO), also known as mitochondrial complex IV, is the terminal enzyme in mitochondrial electron transport chain (ETC). Consistent with the pivotal role of CCO in mitochondrial bioenergetics and high demand for energy to sustain neuronal function, CCO dysfunction has been linked to neurological disorders including AD. However, it remains unclear whether mitochondrial CCO dysfunction represents an adaptive response to AD-associated toxic molecules versus a bona fide pathology to promote AD development. In this study, by meta-analysis of publicly available proteomics analysis of post-mortem frontal lobe tissues from four large cohorts of patients with AD we identified loss of key CCO subunits including mitochondrial DNA (mtDNA)-encoded COX1 and COX3 as well as nuclear DNA (nDNA)-encoded COX5A, COX6B1, COX7C, COX8A, and NDUFA4 in patients with AD. Further biochemical analysis using post-mortem frontal lobe tissues showed lowered CCO activity of neuronal mitochondria from patients with AD, suggesting CCO vulnerability and its potential association with amyloidosis in AD. Lastly, in addition to the inverse relationship between neuronal CCO activity and brain amyloidosis in the tested AD cohort, pharmacological inhibition of CCO promoted amyloid production and elevated beta-secretase 1 (BACE1) activity in cerebral organoids derived from human induced pluripotent stem cells (hiPSCs) from one nonAD and one AD subject. The simplest interpretation of the results is that CCO dysfunction in the frontal lobe is a phenotypic mitochondrial change accompanying AD, which may contribute to the development of brain amyloidosis.

neuroscience↗

bioRxiv2025

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment efficacy and neurotoxicity remains a major clinical challenge due to the complex and heterogeneous nature of the infused CAR-T cell populations. Here, we introduce CART-GPT, a transformer-based model fine-tuned on a curated atlas of 1.12 million CAR-T single-cell RNA-seq profiles annotated with clinical outcomes. CART-GPT is the first AI model developed for CAR-T therapy that predicts both treatment response and the risk of immune effector cell-associated neurotoxicity syndrome (ICANS), achieving state-of-the-art performance (AUC ~0.8) and marking a significant advance in the field. The model provides interpretable insights, revealing that neither therapeutic efficacy nor neurotoxicity is driven by individual cell types alone, but by the combined influence of discrete, distinct subsets across diverse T cell states and transcriptional programs. A novel cell aggregation strategy links single-cell predictions to patient-level metrics, enhancing both accuracy and biological relevance. As a contribution to this ever-evolving field, we also release a comprehensive, annotated single-cell CAR-T atlas as a community resource to facilitate future research in immunotherapy. These advances demonstrate the potential of foundation models in single-cell biology to inform precision CAR-T treatment planning and facilitate the rational design of next-generation cell therapies.

bioinformatics↗

bioRxiv2025

An AI-based and coding-free protocol for forests Leaf Area Index (LAI) calculation

O_LISeasonal and spatial variations in leaf area index (LAI) are challenging to detect in tropical forests due to dynamic lighting conditions and the subtle differences in the variation. Many existing LAI software tools offer one-click processing of all images through auto-threshold segmentation (e.g., HemispheR, HemiPy and Hemisfer), but they produce results with large discrepancies. Some software (e.g. CAN-EYE) requires manual tuning of each image, making large-scale analysis impractical. C_LIO_LIWe analysed 19,000 images from four tropical forest subtypes and found that using coding-free AI software to process hemispherical images can significantly improve the consistency of leaf-sky segmentation, thereby enhancing LAI outcomes. C_LIO_LIThe results show that replacing the auto-threshold with AI substantially reduced inter-software disagreement and delineated correct seasonal and spatial patterns. CAN-EYE was able to identify seasonal patterns but produced less accurate results than the CAN-EYE-AI integrated approach due to subjective user bias. C_LIO_LIThe high consistency achieved through AI integration enables reliable cross-site and cross-operator comparisons. As users can customise the AI model according to local images and combine the AI model with other LAI software, our integrated, affordable, and coding-free method offers wide applicability and high consistency of LAI measurements, facilitating the advancement of tropical forest monitoring and research. C_LI Data/Code for peer review statementOne of the key features of this method is coding-free. The method is explained in Protocolv20251118.docx. We have uploaded R codes for drawing figures in a zip pack. These codes and the protocol will be deposited in the Zenodo (or figshare) database under accession link [TBC]. Since Zenodo allows authors to archive updated versions after publication, we may update the protocol by uploading a revised version to Zenodo. Please check the Zenodo archive for any new versions. In the protocol, we note that users can use Image_conversion_20220407.m and lets_change_values.R instead of the Renormalise function of ilastik to modify values in the classification output images. These codes are not essential for users following our protocol, but could be useful for integrating ilastik with other LAI software not covered in this paper. Additionally, the protocol mentions that Gather_LAI_fapar_from_caneye.R can be used to consolidate output Excel files, eliminating the need to manually open each file. Field measurements of LAI and GCC are available on request.

ecology↗

bioRxiv2025

Quantum algorithm for identifying RNA 3D motifs by processing RNA secondary structure graphs

The identification of RNA motifs plays a crucial role in understanding biological functions, predicting RNA 3D structures, and designing RNA-based therapeutics. One of the efficient strategies approaches this task by analyzing secondary structure graphs annotated with non-canonical base interactions, effectively reframing the problem as a two-dimensional graph-theoretic challenge. However, both the graph isomorphism problem and the maximum common subgraph problem in this domain are classified as NP-complete. Classical computing architectures struggle to address these problems efficiently due to their substantial computational resource demands. In this work, we propose a quantum algorithm specifically designed for processing RNA secondary structure graphs. By encoding the adjacency matrix into a quantum circuit and incorporating base interaction families as edge weights, our approach achieves a resource efficiency of [O](log2 N) in terms of qubits. Numerical simulations demonstrate the algorithms capability to identify 15 distinct motifs across three structural categories: hairpin loops, internal loops, and three-way junction loops. Furthermore, the method successfully detects the common motif between two complete RNA chains. Finally, 29 high conserved RNA motifs were identified in four cell lines of a key lung cancer biomarker gene (MIF), and their potential roles as drug targets were explored. This work presents a highly promising quantum computing framework for addressing the RNA motif identification problem.

bioinformatics↗

bioRxiv2024

Encephalomyocarditis virus non-structural protein 2C degrades NDP52 autophagy protein to promote its own survival

Encephalomyocarditis virus (EMCV) is an important zoonotic pathogen that causes encephalitis and myocarditis, primarily in pigs and in some mammals. Nuclear dot protein (NDP) 52 is an important autophagy adaptor protein that is known to target microbial pathogens, including viruses into autophagosomes to facilitate the selective autophagy process. Here, we wanted to investigate how an important zoonotic virus such as EMCV interacts with NDP52. We found that NDP52 negatively regulates the entry and replication phases of EMCV and interacts with EMCV VP1/VP2 proteins to mediate its autophagic degradation. EMCV counteracts this by exerting autophagy induction through its encoded 2C protein. The autophagy machinery then hijacks NDP52 and transports it to lysosomes for subsequent degradation via late endosomal molecules Rab7 and Rab9. Our study describes a novel mechanism by which EMCV escapes the hosts antiviral response to promote its survival by hijacking the autophagy pathway using the non-structural protein 2C of EMCV. ImportanceEMCV is an important zoonotic pathogen that can induce autophagy, but the regulatory role of autophagy receptors in EMCV infection has been rarely reported. Herein, we show that NDP52 is a novel host limiting factor for EMCV, which not only inhibits early EMCV endocytosis, but also restricts EMCV replication in vitro by controlling autophagic degradation of EMCV structural proteins VP1 and VP2. In order to counteract this, EMCV uses the N-terminal region of its non-structural protein 2C to interact with NDP52, and hijacks the autophagy machine to transport NDP52 to lysosomes for degradation, which weakens NDP52-mediated degradation of VP1 and VP2. Importantly, we reveal a novel mechanism by which EMCV evades the host antiviral response, contributing to a deeper understanding of the infectious mechanism of EMCV and providing new ideas for the design of antiviral drug targets.

microbiology↗

bioRxiv2024

Reinforced identity confers cells with cancer hallmarks

Genetic alterations trigger tumorigenesis in a cell type-specific manner, but the biological basis for this, remains unclear. Here we define reinforced original cell identity established by interaction between genetic alterations and original cell type is a pivotal determinant of tumor development. Using AML-M5MLL-AF9 as a model system, we demonstrated that MLL-AF9 enhanced key transcriptional factors (TFs) expression of promonocyte and cooperated with them to amplify their downstream programs for AML hallmarks. Interactions between MLL-AF9 and promonocyte endowed cancer cells with a reinforced promonocyte identity. Reinforced intrinsic cell identity also sustained cancer hallmarks across cancer types. Furthermore, we identified narciclasine, as an effective disruptor of reinforced promonocyte identity, effectively treating AML-M5MLL-AF9. This study not only identified reinforced cell identity as a fundamental tumor driver beyond genetic mutations but also offers a proof-of-concept for reinforced identity- targeted cancer therapy.

cancer biology↗

bioRxiv2024

Olfactory cortical outputs recruit and shape distinct brain-wide spatiotemporal networks

Odor information is transmitted from the olfactory bulb to several primary olfactory cortical regions in parallel, including the anterior olfactory nucleus (AON) and piriform cortex (Pir). However, the specific roles of the olfactory bulb and cortical outputs in wider interactions with other interconnected regions throughout the brain remain unclear due to the lack of suitable in vivo techniques. Furthermore, emerging associations between olfactory-related dysfunctions and neurological disorders underscore the need for examining olfactory networks at the systems level. Using optogenetics, fMRI, and computational modeling, we interrogated the spatiotemporal properties of brain-wide neural interactions in olfactory networks. We observed distinct downstream recruitment patterns. Specifically, stimulation of excitatory projection neurons in OB predominantly activates primary olfactory network regions, while stimulation of OB afferents in AON and Pir primarily orthodromically activates hippocampal/striatal and limbic networks, respectively. Temporally, repeated OB or AON stimulation diminishes neural activity propagation brain-wide in contrast to Pir stimulation. Dynamic causal modeling analysis reveals a robust inhibitory effect of AON outputs on striatal and limbic network regions. In addition, experiments in aged rat models show decreased brain-wide activation following OB stimulation, particularly in the primary olfactory and limbic networks. Modeling analysis identifies a dysfunctional AON to Pir connection, indicating the impairment of this primary olfactory cortical circuit that disrupts the downstream long-range propagation. Our study delineates the spatiotemporal properties of olfactory neural activity propagation in brain-wide networks for the first time and distinguishes the roles of primary olfactory cortical, AON and Pir, outputs in shaping neural interactions at the systems level.

neuroscience↗

bioRxiv2024

scEccDNAdb: an integrated single-cell eccDNA resource for human and mouse

Extrachromosomal circular DNA (eccDNA), an extrachromosomal circular structured DNA, is extensively found in eukaryotes. Exploring eccDNA at the single-cell level contributes to understanding heterogeneity, evolution, development, and specific functions within cells. Nevertheless, the high-throughput identification methods for single-cell eccDNA are complex, and currently mature and widely applicable technologies are lacking. Those factors have led to a scarcity of resources for studying eccDNA at the single-cell level. Therefore, using available single-cell whole-genome sequencing (WGS) data, we have constructed a comprehensive eccDNA database named scEccDNAdb (https://lcbb.swjtu.edu.cn/scEccDNAdb/). Presently, scEccDNAdb comprises 3,195,464 single-cell eccDNA entries from both disease/health human and mouse samples, which provides comprehensive annotations including oncogenes, typical enhancers, super-enhancers, CTCF binding sites, SNPs, chromatin accessibility, eQTLs, transcription factor binding sites, motifs, and SVs. Additionally, it provides nine online analysis and visualization tools, facilitating the generation of publication-quality figures for eccDNA analysis through the upload of customized files. Overall, scEccDNAdb represents the first comprehensive database known to us for exploring and analyzing single-cell eccDNA data in diverse cell types, tissues, and species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/604058v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@bd23f8org.highwire.dtl.DTLVardef@1847c7eorg.highwire.dtl.DTLVardef@109717forg.highwire.dtl.DTLVardef@fa56b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Inducible biosynthesis of bacterial cellulose in recombinant Enterobacter sp. FY-07

Bacterial cellulose (BC) is an extracellular polysaccharide with myriad unique properties, such as high purity, water-holding capacity and biocompatibility, making it attractive in materials science. However, genetic engineering techniques for BC-producing microorganisms are rare. Herein, the electroporation-based gene transformation and the {lambda} Red-mediated gene knockout method with a nearly 100% recombination efficiency were established in the fast-growing and BC hyperproducer Enterobacter sp. FY-07. This genetic manipulation toolkit was validated by inactivating the protein subunit BcsA in the cellulose synthase complex. Subsequently, the inducible BC-producing strains from glycerol were constructed through inducible expression of the key gene fbp in the gluconeogenesis pathway, which recovered more than 80% of the BC production. Finally, the BC properties analysis results indicated that the induced-synthesized BC pellicles were looser, more porous and reduced crystallinity, which could further broaden the application prospects of BC. To our best knowledge, this is the first attempt to construct the completely inducible BC-producing strains. Our work paves the way for increasing BC productivity by metabolic engineering and broadens the available fabrication methods for BC-based advanced functional materials.

synthetic biology↗

bioRxiv2024

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

neuroscience↗

bioRxiv2024

Intestinal epigenomic alterations are associated with a dysregulated nutrient absorption phenotype in obesity

Obesity is an epidemic with myriad health effects, but little is understood regarding individual obese phenotypes and how they may respond to therapy. Epigenetic changes associated with obesity have been detected in blood, liver, pancreas, and adipose tissues. Previous work using human organoids found that dietary glucose hyperabsorption is a steadfast trait in cultures derived from some obese subjects, but detailed transcriptional or epigenomic features of the intestinal epithelia associated with this persistent phenotype are unknown. This study evaluated differentially expressed genes and relative chromatin accessibility in intestinal organoids established from donors classified as non-obese, obese, or obese hyperabsorptive by body mass index and glucose transport assays. Transcriptomic analysis indicated that obese hyperabsorptive subject organoids have significantly upregulated dietary nutrient absorption transcripts and downregulated type I interferon targets. Chromatin accessibility and transcription factor footprinting predicted that enhanced HNF4G binding may promote the obese hyperabsorption phenotype. Quantitative RT-PCR assessment in organoids representing a larger subject cohort suggested that intestinal epithelial expression of CUBN, GIP, SLC5A11, and SLC2A5 were highly correlated with hyperabsorption. Thus, the obese hyperabsorption phenotype was characterized by transcriptional changes that support increased nutrient uptake by intestinal epithelia, potentially driven by differentially accessible chromatin. Recognizing unique intestinal phenotypes in obesity provides a new perspective in considering therapeutic targets and options to manage the disease.

genomics↗

bioRxiv2024

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging enables high-throughput investigation of neuronal activity, yet its utility is often constrained by a low signal-to-noise ratio (SNR). Conventional denoising algorithms, such as those based on matrix factorization, impose limiting assumptions about the noise process and the spatiotemporal structure of the signal. While deep learning based denoising techniques offer greater adaptability, existing approaches fail to fully exploit the fast temporal dynamics and unique short- and long-range dependencies within voltage imaging datasets. Here, we introduce CellMincer, a novel self-supervised deep learning method designed specifically for denoising voltage imaging datasets. CellMincer operates on the principle of masking and predicting sparse sets of pixels across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without the need for large temporal denoising contexts. We develop and utilize a physics-based simulation framework to generate realistic datasets for rigorous hyperparameter optimization and ablation studies, highlighting the key role of conditioning the denoiser on precomputed spatiotemporal auto-correlations to achieve 3-fold further reduction in noise. Comprehensive benchmarking on both simulated and real voltage imaging datasets, including those with paired patch-clamp electrophysiology (EP) as ground truth, demonstrates CellMincers state-of-the-art performance. It achieves substantial noise reduction across the entire frequency spectrum, enhanced detection of subthreshold events, and superior cross-correlation with ground-truth EP recordings. Finally, we demonstrate how CellMincers addition to a typical voltage imaging data analysis workflow improves neuronal segmentation, peak detection, and ultimately leads to significantly enhanced separation of functional phenotypes.

neuroscience↗

bioRxiv2024

RGS10 deficiency facilitates distant metastasis by inducing epithelial-mesenchymal transition in breast cancer

AbstractDistant metastasis is the major cause of death in patients with breast cancer. Epithelial-mesenchymal transition (EMT) contributes to breast cancer metastasis. Regulator of G protein-signaling (RGS) proteins modulate metastasis in various cancers. This study identified a novel role for RGS10 in EMT and metastasis in breast cancer. RGS10 protein levels were significantly lower in breast cancer tissues compared to normal breast tissues, and deficiency in RGS10 protein predicted a worse prognosis in patients with breast cancer. RGS10 protein levels were lower in the highly aggressive cell line MDA-MB-231 than in the poorly aggressive, less invasive cell lines MCF7 and SKBR3. Silencing RGS10 in SKBR3 cells enhanced EMT and caused SKBR3 cell migration and invasion. The ability of RGS10 to suppress EMT and metastasis in breast cancer was dependent on lipocalin-2 and miR-539-5p. These findings identify RGS10 as a tumor suppressor, prognostic biomarker, and potential therapeutic target for breast cancer.

cancer biology↗

bioRxiv2024

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

biophysics↗

bioRxiv2024

A site-wise reliability analysis of the ABCD diffusion fractional anisotropy data, impact of the scanner and analytical pipeline

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61{+/-}0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76{+/-}0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71{+/-}0.12 vs 0.46{+/-}0.17, p<0.001) and CT (AICC=0.80{+/-}0.10 vs 0.69{+/-}0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

neuroscience↗

bioRxiv2024

Distinct sets of molecular characteristics define tumor-rejecting neoantigens

Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.

immunology↗

bioRxiv2024

3D-Printable Non-invasive Head Immobilization System for Non-Human Primates

A critical contribution to understanding the primate brain is our ability to directly record, manipulate, or image the brain in awake, behaving monkeys. A challenge with carrying out these studies is that typically the monkeys head must be fixed in place. While non-invasive head immobilization systems (NHIS) have been demonstrated to be effective, they have not fully replaced the use of surgically applied headposts. Here, we introduce a novel NHIS, with an option for voluntary engagement, for macaques using a widely available resource: the 3D printer. We designed customized 3D-printable head-immobilization masks for monkeys and a docking system using standard CT scans and user-friendly open-source software packages (FLoRIN, Blender, Python). We examined the efficacy and stability of our mask system for collecting eye fixation data by measuring trial-by-trial gaze precision and accuracy in two mask-immobilized monkeys and three headposted monkeys, including a within-subjects comparison. Our results demonstrate that monkeys stabilized by the mask maintain gaze precision and accuracy comparable with their headposted counterparts. While the headpost outperformed the mask, the difference in precision was 0.03{degrees} of visual angle, and the difference in accuracy was less than 0.2{degrees}, well within the acceptable range for most use-cases. Our process allows for the production of 3D-printable masks based on CT or MRI scans with the flexibility to incorporate design modifications for experimental equipment and resizing, potentially serving as a promising and more easily accessible alternative to headposts and other NHISs for primate neuroscience research.

physiology↗