Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.06.18.448926

Analysis of human brain tissue derived from DBS surgery

Kangas, S. M.·Teppo, J.·Lahtinen, M. J.·Suoranta, A.·Ghimire, B. M.·Mattila, P.·Uusimaa, J.·Varjosalo, M.·Katisko, J.·Hinttala, R.
View original source ↗Export metadata ↓

Abstract

BackgroundTranscriptomic and proteomic profiling of human brain tissue is hindered by availability of fresh samples from living patients. Postmortem samples usually represent the advanced disease stage of the patient. Furthermore, the postmortem interval affects the observed transcriptomic and proteomic profiles. Therefore, access to fresh brain tissue samples from living patients is valuable resource to obtain information on metabolically intact tissue. The implantation of deep brain stimulation (DBS) electrodes into the human brain is a neurosurgical treatment for, e.g., movement disorders. Here, we describe an improved approach to collect brain tissue from surgical instruments used in implantation of DBS device for transcriptomics and proteomics analyses. MethodsSamples were extracted from guide tubes and recording electrodes used in routine DBS implantation procedure that was carried out to treat patients with Parkinsons disease, genetic dystonia and tremor. RNA sequencing was carried out to tissue extracted from the recording microelectrodes and liquid chromatography-mass spectrometry was carried out to analyze tissue from guide tubes. To assess the performance of the current approach, obtained datasets were compared with previously published datasets representing brain tissue. ResultsIn RNA sequencing, altogether 32,034 transcripts representing unique Ensembl gene identifiers were detected from eight samples representing both hemispheres of four patients. By using liquid chromatography-mass spectrometry, we identified 734 unique proteins from 31 samples collected from 14 patients. Comparison with previously published brain derived data indicated that both of our datasets reflected the expected brain tissue specific features. The datasets are available via BioStudies database (accession number S-BSST667). ConclusionsSurgical instruments used in DBS installation retain enough brain material for protein and gene expression studies. Analysis of the datasets indicated that hemisphere-specific expression data can be obtained from individual patients without any sample pooling and without any modifications to the standard surgical protocol. Comparison with previously published datasets obtained with similar approach proved the robustness and reproducibility of the current improved protocol. This approach overcomes the issues that arise from using postmortem tissue, such as effect of postmortem interval, on proteomic and transcriptomic landscape of the brain and can be used for studying molecular aspects of DBS-treatable diseases.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kangas, S. M., Teppo, J., Lahtinen, M. J., Suoranta, A., Ghimire, B. M., Mattila, P., Uusimaa, J., Varjosalo, M., Katisko, J., Hinttala, R.. 2021-06-18. Analysis of human brain tissue derived from DBS surgery. https://doi.org/10.1101/2021.06.18.448926

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗