OSCR

Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 1,693 lines · 58 KB · no license

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: HD-MEA NEUROPulse.py.

Overview

Authors: Eleonora Pali1, Giorgia Pellavio1, Maria Conforti1, Arvin A. Sarkissian2,3, Berna Aliya2,3, Giacomo Sciacca4, Supriya S. Wariyar2,3, Francesco Mainardi4, Mariateresa Tedesco4, Ivan Verduci4, Gendenver Cadiao4, Chiara Cervetto5,6, Jimena Andersen2,3, Fikri Birey2,3, Alessandro Maccione4, Egidio D’Angelo1,7, Lisa Mapelli1
  1. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
  2. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
  3. Emory Brain Organoid Hub, Atlanta, GA, USA
  4. Brain AG, Pfäffikon, Switzerland
  5. Department of Pharmacology (DIFAR), University of Genoa, Genoa, Italy
  6. Interuniversity Center for the Promotion of the 3Rs Principles in Teaching and Research (Centro 3R), Italy
  7. Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
Institutions: University of Pavia (Italy); Emory University (United States); 3Brain (Switzerland) (Switzerland); University of Genoa (Italy)
Journal: Bio-protocol, volume 16, issue 11, article e5708
Dates: received 13 February 2026; accepted 29 April 2026; published online 5 June 2026
Type: Methods article · Language: English
License: CC BY-NC
Identifiers: DOI 10.21769/bioprotoc.5708 · PMID 42305135 · PMCID PMC13266469 · OpenAlex W7161173022
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: 3D high-density multielectrode array, Acute slices, Brain spheroids, Neural organoids, Electrophysiological recordings, Electrophysiological data analysis, Acute electrophysiological measurements
Journal subjects: Biology, Clinical Protocols
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Animal and human stem cell–derived three-dimensional models to study physio-pathological brain functioning are becoming a gold standard for in vitro electrophysiology, as they enable the recapitulation of complex network properties by accounting for spatial architectural features that better reflect in vivo conditions than simpler 2D models. Standard planar multielectrode arrays (MEAs), typically providing tens of recording electrodes, are commonly used to record activity from 2D neuronal cultures. However, when adapted for use with 3D models, planar 2D MEAs showed limited effectiveness. The main issues are limited specimen adhesion to the chip, a low number of sensing elements, inability to retrieve signals from within the tissue, and reduced perfusion and vitality of the tissue in contact with sensors. To overcome these limitations, a new generation of microchip-based 3D high-density MEAs (3D HD-MEA) has been developed and validated in recent years. This technological advancement has improved the sensing capabilities and the vitality of 3D models, providing a tool tailored to maximize their potential. Here, we present an optimized protocol for neural network activity recordings in 3D models (including acute slices, brain spheroids, and organoids) from various brain regions using 3D HD-MEAs. First, we summarize the critical steps for 1) obtaining viable acute slices from the mouse cerebellum, cortico-hippocampal circuit, and prefrontal cortex, 2) establishing efficient coupling of the slices with the chip, and 3) performing recordings and analyses. We then describe the main procedures required to obtain human and animal brain spheroids and neural organoids, as well as standardized routines to perform effective recordings and analyses. For each section, we highlight the crucial steps, identify tips for specific applications, and propose troubleshooting procedures. For example, the same type of preparation (e.g., acute slices) requires different adjustments when working with different brain areas. The specific information provided here is intended to assist researchers in their daily efforts to obtain efficient and reproducible functional recordings from 3D models by using the cutting-edge technique of 3D HD-MEA.

Key features

• Comprehensive all-in-one guide covering the complete workflow for acquiring electrophysiological data from brain slices, neural region-specific organoids, and brain spheroids.

• Intuitive, step-by-step protocol for brain slice preparation, enriched with practical tips and expert recommendations to ensure high-quality tissue viability.

• Detailed instructions for optimal use of 3D HD-MEA technology, including proper handling of the sample holder for recordings from brain slices, neural organoids, and spheroids.

• In-depth guidance on BrainWave6 software, providing clear procedures for data acquisition, signal detection, and advanced electrophysiological analysis across all sample types.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 13908319

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 16 files, 1 script
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: h5py (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at record 13908319, when its fingerprint is the one OSCR verified. How this works.

At the source:

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 7 keywords, 38 references.

Cite

This paper

Pali, E., Pellavio, G., Conforti, M., Sarkissian, A. A., Aliya, B., Sciacca, G., Wariyar, S. S., Mainardi, F., Tedesco, M., Verduci, I., Cadiao, G., Cervetto, C., Andersen, J., Birey, F., Maccione, A., D’Angelo, E., & Mapelli, L. (2026). Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays. Bio-protocol, 16(11), e5708. https://doi.org/10.21769/bioprotoc.5708

BibTeX

@article{pali2026measuring,
author = {Pali, Eleonora and Pellavio, Giorgia and Conforti, Maria and Sarkissian, Arvin A. and Aliya, Berna and Sciacca, Giacomo and Wariyar, Supriya S. and Mainardi, Francesco and Tedesco, Mariateresa and Verduci, Ivan and Cadiao, Gendenver and Cervetto, Chiara and Andersen, Jimena and Birey, Fikri and Maccione, Alessandro and D’Angelo, Egidio and Mapelli, Lisa},
title = {{Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays}},
journal = {Bio-protocol},
year = {2026},
month = jun,
volume = {16},
number = {11},
pages = {e5708},
publisher = {Bio-protocol, LLC},
issn = {2331-8325},
doi = {10.21769/bioprotoc.5708},
url = {https://doi.org/10.21769/bioprotoc.5708},
pmid = {42305135},
pmcid = {PMC13266469}
}

RIS

TY - JOUR
AU - Pali, Eleonora
AU - Pellavio, Giorgia
AU - Conforti, Maria
AU - Sarkissian, Arvin A.
AU - Aliya, Berna
AU - Sciacca, Giacomo
AU - Wariyar, Supriya S.
AU - Mainardi, Francesco
AU - Tedesco, Mariateresa
AU - Verduci, Ivan
AU - Cadiao, Gendenver
AU - Cervetto, Chiara
AU - Andersen, Jimena
AU - Birey, Fikri
AU - Maccione, Alessandro
AU - D’Angelo, Egidio
AU - Mapelli, Lisa
TI - Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays
T2 - Bio-protocol
J2 - Bio Protoc
PY - 2026
DA - 2026/06/05
VL - 16
IS - 11
SP - e5708
SN - 2331-8325
PB - Bio-protocol, LLC
DO - 10.21769/bioprotoc.5708
UR - https://doi.org/10.21769/bioprotoc.5708
LA - en
ER -

CSL-JSON

{
"id": "10.21769/bioprotoc.5708",
"type": "article-journal",
"title": "Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays",
"container-title": "Bio-protocol",
"author": [
{
"family": "Pali",
"given": "Eleonora"
},
{
"family": "Pellavio",
"given": "Giorgia"
},
{
"family": "Conforti",
"given": "Maria"
},
{
"family": "Sarkissian",
"given": "Arvin A."
},
{
"family": "Aliya",
"given": "Berna"
},
{
"family": "Sciacca",
"given": "Giacomo"
},
{
"family": "Wariyar",
"given": "Supriya S."
},
{
"family": "Mainardi",
"given": "Francesco"
},
{
"family": "Tedesco",
"given": "Mariateresa"
},
{
"family": "Verduci",
"given": "Ivan"
},
{
"family": "Cadiao",
"given": "Gendenver"
},
{
"family": "Cervetto",
"given": "Chiara"
},
{
"family": "Andersen",
"given": "Jimena"
},
{
"family": "Birey",
"given": "Fikri"
},
{
"family": "Maccione",
"given": "Alessandro"
},
{
"family": "D’Angelo",
"given": "Egidio"
},
{
"family": "Mapelli",
"given": "Lisa"
}
],
"container-title-short": "Bio Protoc",
"volume": "16",
"issue": "11",
"page": "e5708",
"DOI": "10.21769/bioprotoc.5708",
"PMID": "42305135",
"PMCID": "PMC13266469",
"ISSN": "2331-8325",
"publisher": "Bio-protocol, LLC",
"URL": "https://doi.org/10.21769/bioprotoc.5708",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
5
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pbio.3003757 [code]
Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.
Journal: PLoS biology
In common: h5py, pandas, SciPy, 2 other tools, 3 references
[2] doi:10.1038/s41467-026-71458-0 [code]
Early differential impact of MeCP2 mutations on functional networks in Rett syndrome patient-derived human cortical organoids.
Journal: Nature communications
In common: h5py, SciPy, Matplotlib, 1 other tool, 2 references
[3] doi:10.7554/elife.110588 [code]
Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP), methods / tools
[4] doi:10.1093/bioinformatics/btag570 [code]
CASCADE: criticality avalanche spike cross-platform analysis detection engine, a multi-manufacturer MEA bash analysis pipeline.
Journal: Bioinformatics (Oxford, England)
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP), methods / tools
[5] doi:10.1002/advs.202522762 [code]
Enhancing Maturation of Human Neuromuscular Organoids via Electrical Stimulation.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: h5py, pandas, SciPy, 2 other tools, 1 reference
[6] doi:10.1038/s41467-026-74320-5 [code]
Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development.
Journal: Nature communications
In common: h5py, pandas, SciPy, 2 other tools, 1 reference
[7] doi:10.1038/s41531-026-01531-4 [code]
Inconsistent subthalamic local field potential beta activity amid in- and antiphasic neuronal bursts.
Journal: NPJ Parkinson's disease
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP)
[8] doi:10.1016/j.patter.2026.101590 [code]
Density-based longitudinal neuron tracking in high-density electrophysiological recordings.
Journal: Patterns (New York, N.Y.)
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP)
[9] doi:10.1038/s41592-026-03076-z [code]
Neuropixels Opto: combining high-resolution electrophysiology and optogenetics.
Journal: Nature methods
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP)
[10] doi:10.1038/s41586-026-10331-y [code]
Active dissociation of intracortical spiking and high gamma activity.
Journal: Nature
In common: h5py, pandas, SciPy, 2 other tools, extracellular electrophysiology (units, LFP)

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.