BRAIN organoids unTanglement with SERS

Projektinformationen

BRAINtSERS

ID Finanzhilfevereinbarung: 101030325

DOI

10.3030/101030325

Projekt abgeschlossen

EK-Unterschriftsdatum 25 Februar 2021

Startdatum 1 Dezember 2021

Enddatum 31 Dezember 2023

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 175 255,68

EU-Beitrag

€ 175 255,68

Koordiniert durch

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Italy

Periodic Reporting for period 2 - BRAINtSERS (BRAIN organoids unTanglement with SERS)

Berichtszeitraum: 2023-01-01 bis 2023-12-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Neurological disorders remain a leading cause of mortality due to the lack of effective treatments, which is attributed to the limitations of current research models for neurological disorders, neurodevelopment, and drug interactions in the central nervous system. In vitro monolayer cell cultures and in vivo animal models have restrictions that impede translation of research and treatment to humans. The genetic and environmental cues in animals do not match those of humans, and two-dimensional cultures lack cellular diversity and interconnectivity, particularly in brain models. Human brain organoids provide an unprecedentedly accurate model for human brain diseases and developmental programs. In addition, Brain organoids derived from hiPSCs (human induced pluripotent stem cells) can mimic the exact genetic mutations and developmental progression of a disorder derived from a patient's genome, creating great prospects for patient-specific treatments and transplantation therapy. However, current techniques for analyzing brain organoids during growth and pathology evolution, such as imaging and genomic analysis, have limitations. Fluorescent imaging requires the labeling of target molecules, which can alter their behavior and introduce artifacts, while genetic sequencing irreversibly alters the sample, making it impossible to conduct further experiments on the same material.

Therefore, it is imperative, especially for brain organoid to find methodologies that can fully characterize their structural and functional properties without interfering with the development. Raman Spectroscopy, being non-invasive and label free, could address these challenges allowing to get the whole biochemical information along the development of the organoid. Specifically, the action will provide: (i) a label-free, highly sensitive technique for quantification and detection of biomolecules in brain organoids, (ii) a minimally-invasive system for cellular phenotyping, thus a non-destructive method for characterization of brain organoids during developmental stages.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

To establish a label-free methodology for biomolecule detection in brain organoids, the researcher conducted, for the first time, Raman spectroscopy on hiPSC and hESC-derived brain organoids at various developmental stages. Cortical organoids were derived by dissociating pluripotent stem cell colonies and subjected to modified differentiation protocols to ensure functional maturation. While data were collected using a custom-made Raman microscope coupled with a fluorescent microscope. Data analysis was conducted using MATLAB and Python, involving signal preprocessing, dimensionality reduction techniques, and Machine Learning algorithms.
Therefore, the developed Raman analysis pipeline was utilized to differentiate between organoid maturation states, comparing stages ranging from 45 to 150 days for hiPSC organoids and from 120 to 150 days for hESC organoids. The analysis unveiled distinct differences in glycogen and protein content across maturation stages, serving as indicators of increased functional activity and reduced cycling cell numbers throughout development. These findings were consistent across both cell lines and are currently undergoing submission for publication.
In parallel, the Raman pipeline allowed the label free identification of the nuclear compartment of the whole cell diversity in these organoids. This represents an initial step towards achieving single-cell resolution in the biochemical analysis provided by this model.
Furthermore, preliminary findings from the evaluation of the optimal polymer for organoid interfacing during development have been acquired. Indeed, the stiffness of the polymer is paramount, as rigid polymers have been observed to guide cell differentiation towards specific directions. Conversely, softer polymers with a Young's modulus comparable to the cells under study provide a less disruptive environment for the system.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Over the last few decades, Raman spectroscopy has gain significant attention in the field of biology and medicine, thanks to the advances in detection technologies and the use of machine learning. This technique offers a comprehensive understanding of the dynamics of biomarkers, such as lipids, nucleic acids or other metabolites within the sample. It can be used as a complementary approach alongside of multi-omics to have a more comprehensive information or as an alternative of fluorescence for label free imaging. This is particularly valuable when studying dynamic system, such as maturation of brain organoids. By analyzing biochemical changes in brain organoids using Raman spectroscopy, this project will pave the way for broader use of the technique in the field.
The researcher conducted a thorough biochemical analysis of brain organoids using a multimodal methodology. The end goal is to support dynamic analysis of brain organoids and expand the methodology to drug-treated organoids to demonstrate its applicability in therapeutics and drug discovery. Moreover, the spatial distribution of the target biomarkers was investigated. Simultaneously, a machine learning-based protocol will be devised to automate the technique, thereby enabling its widespread adoption.

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