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Di Bella DJ, Domínguez-Iturza N, Brown JR, Arlotta P. Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex. Neuron 2024; 112:2091-2111. [PMID: 38754415 DOI: 10.1016/j.neuron.2024.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/28/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.
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Affiliation(s)
- Daniela J Di Bella
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Nuria Domínguez-Iturza
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Juliana R Brown
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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Martini L, Amprimo G, Di Carlo S, Olmo G, Ferraris C, Savino A, Bardini R. Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states. Comput Biol Med 2024; 168:107783. [PMID: 38056213 DOI: 10.1016/j.compbiomed.2023.107783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/23/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.
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Affiliation(s)
- Lorenzo Martini
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy.
| | - Gianluca Amprimo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy; Institute of Electronics, Information Engineering and Telecommunications, National Research Council, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy.
| | - Stefano Di Carlo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.smilies.polito.it
| | - Gabriella Olmo
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.sysbio.polito.it/analytics-technologies-health/
| | - Claudia Ferraris
- Institute of Electronics, Information Engineering and Telecommunications, National Research Council, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy. https://www.ieiit.cnr.it/people/Ferraris-Claudia
| | - Alessandro Savino
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. https://www.smilies.polito.it
| | - Roberta Bardini
- Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy.
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Rueda C, Rodríguez-Collado A. Functional clustering of neuronal signals with FMM mixture models. Heliyon 2023; 9:e20639. [PMID: 37867904 PMCID: PMC10589779 DOI: 10.1016/j.heliyon.2023.e20639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 09/21/2023] [Accepted: 10/03/2023] [Indexed: 10/24/2023] Open
Abstract
The identification of unlabeled neuronal electric signals is one of the most challenging open problems in neuroscience, widely known as Spike Sorting. Motivated to solve this problem, we propose a model-based approach within the mixture modeling framework for clustering oscillatory functional data called MixFMM. The core of the approach is the FMM (Frequency Modulated Möbius) waves, which are non-linear parametric time functions, flexible enough to describe different oscillatory patterns and simple enough to be estimated efficiently. In particular, specific model parameters describe the phase, amplitude and shape of the waveforms. A mixture model is defined using FMM waves as basic functions and gaussian errors, and an EM algorithm is proposed for estimating the parameters. Spike Sorting (SS) has received considerable attention in the literature, and different functional clustering approaches have been considered. We have conducted a fair comparative analysis of the MixFMM with three competitors. Two of them are traditional methods in functional clustering and widely used in Spike Sorting. The third is an approach that has proven superior to many others solving Spike Sorting problems. The datasets used for validation include benchmarking simulated and real cases. The internal and external validation indexes confirm a better performance of the MixFMM on real data sets against the three competitors and an outstanding performance in simulated data against traditional approaches.
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Affiliation(s)
- Cristina Rueda
- Department of Statistics and Operations Research, University of Valladolid, 47011 Valladolid, Spain
- Mathematics Research Institute of the University of Valladolid (IMUVA), 47011 Valladolid, Spain
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