Biology subjects

Chen, K.-P.

Publications and source records attributed to Chen, K.-P..

4 recordsLinked to original sources

bioRxiv2026

Extending Kilosort for 3D Neural Probes

Kilosort 2.0 is a widely adopted spike sorting algorithm recognized for its efficiency and accuracy on planar electrode arrays, such as Neuropixels. To adapt its robust architecture to emerging three-dimensional (3D) neural probes, we present Kilosort 2.0-3D, a modified version that leverages 3D spatial information. Our modification specifically redefines the spatial processing components of Kilosort 2.0 to operate in 3D space while leaving the core template-matching process unchanged. By using synthetic extracellular recording data with ground-truth neuron positions and firing times, we demonstrate that Kilosort 2.0-3D effectively resolves spatial ambiguities and unit misclassifications inherent in 2D spatial assumptions. Our results show that Kilosort 2.0-3D achieves rotational invariance and maintains full backward compatibility with planar arrays. This work establishes a validated, scalable tool for spike sorting of high-density 3D neural electrophysiology data.

neuroscience↗

bioRxiv2025

Airway microbiome-host transcriptome networks link microbial dysbiosis to survival outcomes and therapeutic opportunities in severe COVID-19

The lower respiratory tract microbiome may critically influence outcomes in severe COVID-19, yet remains understudied. We analyzed bronchoalveolar lavage fluid from 86 severe patients requiring mechanical ventilation, stratified into two groups. We identified 213 bacteria with differential abundance, 11 bacteria associated with differentially expressed genes, and 39 of them were associated with patient survival. The more serious group exhibited reduced microbial network complexity, suggesting ecosystem disruption. To explore therapeutic potential, we employed our previously published model and predicted that compounds like palbociclib, XMD-1499, RS-17053, and RS-504393 may shift host gene expression favoring beneficial microbial signatures. These results highlight an important link between microbial dysbiosis and COVID-19 severity, offering promising directions for microbiome-informed therapeutic strategies. Our findings underscore the importance of the lung microbiome in modulating host responses and clinical outcomes, and suggest that modulating microbial-host interactions may serve as a novel adjunctive approach in the treatment of severe COVID-19. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/661228v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@2fc879org.highwire.dtl.DTLVardef@1128d5dorg.highwire.dtl.DTLVardef@fef0eaorg.highwire.dtl.DTLVardef@17aa499_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Real-time spike sorting with 3D neural probe and triangulation localization

A key challenge in correlating neuronal activity with brain function is the limited sampling probability of neuronal activity in real time. It is crucial to increase the sampling probability substantially and in real time. We hypothesized that 3-dimensional (3D) neural probes offer faster and stronger prospects for cell yield than 2D electrode arrays. We simulated a 1000-neuron neuronal network, mimicking the granular layer of the barrel cortical column, recorded signals from inserted 384 electrodes (organized in 3D or 2D), and sorted units using Kilosrt or triangulation localization. We demonstrated that 3D electrode arrays converge more space for triangulation spike sorting than 2D probes do. 3D neural probes, together with triangulation, could isolate up to 80% of the simulated 1000 neurons (as ground truth) and have a cell yield of up to 5, which is, to the best of our knowledge, significantly higher than standard 2D electrodes with Kilosort or triangulation. With a signal-to-noise ratio (SNR) of 10, which is close to the real world, the simulation data suggest that 3D electrode arrays in a face-centric cubic (FCC) arrangement provide a better cell yield. However, larger background noise (e.g. an SNR of 1, which can be improved with lower electrode impedance) has a stronger impact on the triangulation spike sorting. Since only the peak value of spikes are required for triangulation localization, the computing loading is much less than spike waveform-based spike sorting approach. Thus, combining 3D electrode arrays with triangulation localization is ideal for real-time spike sorting. Thus, we demonstrated that adding one more dimension in designing neural probes can dramatically increase cell yield and speed up isolating neuronal unit activity. We, for the first time, provide a tool for utilizing computer simulations to optimize the design of neural electrode arrays before time-consuming probe fabrication.

neuroscience↗

bioRxiv2019

Characterizing the Cancer-Associated Microbiome with Small RNA Sequencing Data

The microbiome is recognized as a quasi-organ in the human body. In particular, the gut microbiome is correlated with immune function, metabolism, and tumorigenesis. When dysbiosis of the microbiome occurs, this variation may contribute to alterations in the microenvironment, potentially inducing an inflammatory immune response and providing a niche for neoplastic growth. However, there is limited evidence regarding the correlation and interaction between the microbiome and tumorigenesis. By utilizing microRNA sequencing data of patients with colon and rectal cancer from The Cancer Genome Atlas, we designed a novel analytical process to extract non-human small RNA sequences and align them with the microbial genome to obtain a comprehensive view of the cancer-associated microbiome. In the present study, we identified > 1000 genera among 630 colorectal samples and clustered these samples into three distinctive colorectal enterotypes. Each cluster has its own distinctive microbial composition and interactions. Furthermore, we found 12 genera from these clusters that are associated with cancer stages and revealed their putative functions. Our results indicate that the proposed analytical approach can effectively determine the cancer-associated microbiome. It may be readily applied to explore other types of cancer, in which specimens of the microbiome are difficult to collect.

bioinformatics↗