Evidence map›Paper›PMID 40085717›Full record

ArticleScience advances2025

PseudoSorter: A self-supervised spike sorting approach applied to reveal Tau-induced reductions in neuronal activity.

Marius Brockhoff, Jakob Träuble, Sagnik Middya, Tanja Fuchsberger, Ana Fernandez-Villegas, Amberley Stephens, Miranda Robbins, Wenyue Dai, Belquis Haider, Sulay Vora and 5 more

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Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Marius BrockhoffDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0009-0004-3914-2724
Jakob TräubleDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Sagnik MiddyaDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Tanja FuchsbergerDepartment of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.ORCID 0000-0002-4751-8806
Ana Fernandez-VillegasDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0000-0002-9766-8722
Amberley StephensDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Miranda RobbinsDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0000-0002-6576-5763
Wenyue DaiDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0009-0001-1437-1887
Belquis HaiderDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0009-0005-9440-0842
Sulay VoraDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0009-0001-2437-5507
Nino F LäubliDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0000-0003-2894-2385
Clemens F KaminskiDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0000-0002-5194-0962
George G MalliarasElectrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK.ORCID 0000-0002-4582-8501
Ole PaulsenDepartment of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.ORCID 0000-0002-2258-5455
Gabriele S Kaminski SchierleDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.ORCID 0000-0002-1843-2202

Funding

Wellcome Trust
6 · The paper itself

Abstract

Microelectrode arrays (MEAs) permit recordings with high electrode counts, thus generating complex datasets that would benefit from precise neuronal spike sorting for meaningful data extraction. Nevertheless, conventional spike sorting methods face limitations in recognizing diverse spike shapes. Here, we introduce PseudoSorter, which uses self-supervised learning techniques, a density-based pseudolabeling strategy, and an iterative fine-tuning process to enhance spike sorting accuracy. Through benchmarking, we demonstrate the superior performance of PseudoSorter compared to other spike sorting algorithms before applying PseudoSorter on MEA recordings from hippocampal neurons exposed to subneuronal concentrations of monomeric Tau as a model for Alzheimer's disease. Our results unveil that Tau diminishes the firing rate of a subset of neurons, which complement our findings observed using conventional electrophysiology analysis, and demonstrate that PseudoSorter's high accuracy and throughput make it a valuable tool for studying neurodegenerative diseases, enhancing our understanding of their underlying mechanisms, as well as for therapeutic drug screening.

Indexed as

Action PotentialsNeuronstau ProteinsAlgorithmsAlzheimer DiseaseAnimalsHippocampusHumansMicroelectrodesRatstau Proteins

Identifiers

PMID40085717
PMCPMC11908484

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.