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Biology subjects

Sandeep, S.

Publications and source records attributed to Sandeep, S..

3 recordsLinked to original sources

Deep Brain Stimulation Microelectrodes as a Source of Human Subcortical RNA: Validation of a Low-Input Transcriptomic Protocol

BackgroundUnderstanding the molecular basis of Parkinsons disease (PD) phenotypic heterogeneity maybe improved by in vivo access to deep brain tissue. Deep brain stimulation (DBS) surgery offers a unique opportunity: microelectrodes traversing the subthalamic nucleus (STN) carry adherent brain tissue upon withdrawal, providing a source of RNA from subcortical regions in living patients without additional invasive procedures. AimTo develop and validate a low-input RNA extraction and transcriptomic profiling protocol using DBS microelectrodes in post-mortem human brain, to confirm that recovered RNA is of brain rather than blood origin, and to characterise its sub-regional and cellular identity. MethodologyDBS microelectrodes were inserted without image guidance or guide tube into three unfixed post-mortem human brains targeting the STN trajectory. In clinical practice, a guide tube shields the electrode from cortical tissue; its absence here means tissue from the full insertion trajectory may contribute to recovered RNA. Thirty-eight microelectrodes were evaluated across single and pooled strategies. RNA was extracted using a modified RNeasy Micro low-input protocol; libraries prepared using NEBNext Single Cell/Low Input RNA Library Kit and sequenced on Illumina NovaSeq 6000 (paired-end, 2x150 bp). Tissue identity was validated against GTEx v10 (54 tissues) and Allen Human Brain Atlas (ABA, 19 subcortical regions including STN) using Pearson correlation with permutation testing (1,000 permutations) and BH-FDR correction. Transcriptional overlap between subcortical and cortical reference regions was quantified and subcortical-enriched gene filtering performed. ResultsTwenty-five RNA isolates were obtained from 38 microelectrodes; 54.5% of Bioanalyzer-assessed samples achieved RIN [&ge;]5 (median 7.1; range: 5.9-7.8). Fifteen libraries passed sequencing QC (mean depth 43.0 {+/-} 14.1 million read pairs; mean Q30 83.2 {+/-} 5.6 %). PCA of rlog transformed expression data resolved samples by donor identity. All samples confirmed brain tissue origin by GTEx {tau}-index tissue specificity correlation (n=996 brain and blood specific markers). Brain correlations were highest for frontal cortex (mean r= 0.683 {+/-} 0.072), anterior cingulate cortex (mean r= 0.658 {+/-} 0.069), amygdala (mean r= 0.615 {+/-} 0.068), and basal ganglia including caudate (mean r= 0.553 {+/-} 0.059), putamen (mean r= 0.546 {+/-} 0.059), and nucleus accumbens (mean r= 0.544 {+/-} 0.057); all BH-FDR < 0.05. Cerebellum showed the lowest brain correlations (cerebellar hemisphere: mean r = 0.327 {+/-} 0.039; cerebellum: mean r = 0.314 {+/-} 0.037). Whole blood correlation was strongly negative (mean r= -0.250 {+/-} 0.079), confirming non-haematological origin. ABA genome-wide analysis confirmed positive STN correlation (r=0.54-0.62; permutation p<0.001). MuSiC cell-type deconvolution against the Allen Brain Atlas HMBA-BG snRNA-seq reference identified oligodendrocytes (33.5 {+/-} 17.2%), frontal cortical neurons (9.1 {+/-} 7.0%), STR D1 MSNs (6.0 {+/-} 4.9%), and dopaminergic neurons (1.1{+/-} 2.7%) as the principal cell types, independently corroborating bulk transcriptomic findings at single-cell resolution. ConclusionThis study validates a low-input RNA extraction protocol for DBS microelectrodes, confirming brain-specific transcriptomic profiles consistent with STN-adjacent subcortical sampling. The 96% transcriptional overlap between cortical and subcortical regions, combined with absence of a guide tube in this post-mortem model, limits sub-regional specificity; clinical application with a guide tube would enrich the subcortical signal. These findings provide the methodological framework for in vivo molecular profiling of the human basal ganglia during DBS surgery in PD patients.

genetics↗

Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?

Determining an optimal protein configuration for the employment of protein binding analysis as completed by Temperature based Replica Exchange Molecular Dynamics (T-REMD) is an important process in the accurate depiction of a proteins behavior in different solvent environments, especially when determining a proteins top binding sites for use in protein-ligand and protein-protein docking studies. However, the completion of this analysis, which pushes out top binding sites through configurational changes, is an polynomial-state computational problem that can take multiple hours to compute, even on the fastest supercomputers. In this study, we aim to determine if graph cutting provide approximated solutions the MaxCut problem can be used as a method to provide similar results to T-REMD in the determination of top binding sites of Surfactant Protein A (SP-A) for binding analysis. Additionally, we utilize a quantum-hybrid algorithm within Iff Technologys Polar+ package using an actual quantum processor unit (QPU), an implementation of Polar+ using an emulated QPU, or Quantum Abstract Machine (QAM), on a large scale classical computing device, and an implementation of a classical MaxCut algorithm on a supercomputer in order to determine the types of advantages that can be gained through using a quantum computing device for this problem, or even using quantum algorithms on a classical device. This study found that Polar+ provides a dramatic speedup over a classical implementation of a MaxCut approximation algorithm or the use of GROMACS T-REMD, and produces viable results, in both its QPU and QAM implementations. However, the lack of direct configurational changes carried out onto the structure of SP-A after the use of graph cutting methods produces different final binding results than those produced by GROMACS T-REMD. Thus, further work needs to be completed into translating quantum-based probabilities into configurational changes based on a variety of noise conditions to better determine the accuracy advantage that quantum algorithms and quantum devices can provide in the near future.

bioinformatics↗

Utilizing Quantum Biological Techniques on a Rigetti QPUfor Improved Protein Binding Simulation

Iff Technologies has constructed a tool named Polar+ that can predict protein-to-protein binding that operates faster and at a higher quality than the prominent industry standards for protein binding, including Autodock Vina and SwissDock. The ability to provide this advantage over other market leaders comes from a new approach to biophysics, dubbed many-body biological quantum systems, that are modeled using quantum processing units and quantum algorithms provided by Rigetti. This paper provides both experimental and theoretical evidence behind the validity of the quantum biology approach to protein modeling, an overview of the first experimental work completed by Polar+, and a review of results obtained compared to other tools and data found in the lab.

bioinformatics↗