Sofie R. Salama’s research while affiliated with University of California, Santa Cruz and other places

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Publications (181)


Fig. 1 | Summary of CARE analysis results and treatment outcomes. a CARE pathway enrichment summary. Significantly overexpressed genes are colored according to the legend. Pathway enrichment was assessed by the Molecular Signatures Database. Expression in log 2 (TPM + 1) for individual genes is reported in square brackets. Multiple receptor tyrosine kinases (FGFR1, FGFR2, PDGFRA) were found to be overexpression outliers, consistent with pan-disease enrichment in the "Reactome Signaling by FGFR In Disease" pathway and the "Biocarta PDGF
Fig. 2 | Gene expression levels in the patient's RNA-Seq dataset relative to comparator cohorts. The expression of each gene of interest (outlier or implicated by pathway analysis) in the patient's RNA-Seq dataset (TH34_1352_S01) is denoted with a vertical red line plotted with respect to the gene's expression in log2(TPM+1)
Fig. 3 | Immunohistochemistry stains for CDK4 expression. a Histologic sections of metastatic tumor in the lung demonstrate cords and nests of epithelioid cells with clear cytoplasm. An immunohistochemical stain for CDK4 shows brown staining indicating overexpression in tumor cells (b), compared to non-neoplastic lung parenchyma (c). a Hematoxylin and eosin stain, 20x magnification; b, c, immunoperoxidase stain with hematoxylin counterstain, 20x magnification).
Comparative analysis of RNA expression identifies effective targeted drug in myoepithelial carcinoma
  • Article
  • Full-text available

May 2025

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11 Reads

npj Precision Oncology

Yvonne A. Vasquez

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Lauren Sanders

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Holly C. Beale

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[...]

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Sheri L. Spunt

Myoepithelial carcinoma is an ultra-rare pediatric solid tumor with no targeted treatments. Clinical implementation of tumor RNA sequencing (RNA-Seq) for identifying therapeutic targets is underexplored in pediatric cancer. We previously published the Comparative Analysis of RNA Expression (CARE), a framework for incorporating RNA-Seq-derived gene expression into the clinic for difficult-to-treat pediatric cancers. Here, we discuss a 4-year-old male diagnosed with myoepithelial carcinoma who was treated at Stanford Medicine Children’s Health. A metastatic lung nodule from the patient underwent standard-of-care tumor DNA profiling and CARE analysis, wherein the patient’s tumor RNA-Seq profile was compared to over 11,000 uniformly analyzed tumor profiles from public data repositories. DNA profiling yielded no actionable mutations. CARE identified overexpression biomarkers and nominated a treatment that produced a durable clinical response. These findings underscore the utility of data sharing and concurrent analysis of large genomic datasets for clinical benefit, particularly for rare cancers with unknown biological drivers.

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Figure 2. Electrophysiological characterization of dorsal forebrain organoid development. (A) Schematic of the recording setup using an HD-MEA chip. (B) Representative raster plot showing neuronal activity, with the population firing rate over time (blue). Units sorted by mean firing rate. (C) Spike time tiling coefficient (STTC) matrix showing correlation between unit spike trains, sorted by mean firing rate. (D-E) Violin plots showing log transformed mean firing rates (Hz) (D) and log transformed mean STTC (E) over early (23-33 days), mid (34-45 days), and late (46-64 days). (n = 16 organoids, 28,809 units) (F-G) Linear mixed-effects model predicted line plot of the log transformed mean firing rate distribution (F) and log transformed STTC (G). ns = not significant, * Significant after Bonferroni correction p < 0.017, Kolmogorov-Smirnov test (D-E), Mixed-effects model (F-G). Data shown as mean ± CI.
Figure 6. Distinct network topologies highlight organizational differences between dorsal and ventral forebrain organoids. (A) Schematic representations of different network topologies: Regular (Top), Small-World (Middle), and Random (Bottom). (B) Violin plots showing the distribution of small-world index (S) (Left) for DF (Top) and VF (Bottom), clustering coefficient (C) (Center), and path length (L) (Right), each normalized against random surrogate networks. *p < 0.0167, **p < 0.0033, ***p < 0.00033 (Bonferroni corrected), Mixed-effects model.
Figure 7. Divergent Core-Periphery Organization Reveals Distinct Network Specialization in Dorsal and Ventral Forebrain Organoids. (A) Schematic representation of the k-core algorithm used to identify core and peripheral regions within neural networks. (B) Violin plots showing core/periphery density measures across developmental stages (23-33, 34-45, and 46-64 days) for DF (green) and VF (purple) organoids. (C) Representative force-directed graph visualizations of core/periphery labeled nodes showing age group 46-64 DF (Top) core (dark green), DF periphery (blue), VF (Bottom) core (purple), and VF periphery (yellow) regions. *p < 0.05, **p < 0.001, ***p < 0.0001, Mixed-effects model
Figure 9. Functional community structure reveals functional differences between dorsal and ventral forebrain networks (A) Network community structure of DF organoids at age group 46-64 showing a densely integrated organization with extensive interconnections between modules. (Top) Force-directed graph representation of STTC-derived network structure with node colors representing different modules. (Bottom) Representative time-series showing concurrent activity across modules, with Module 4 (green) and Module 6 (red) displaying highly correlated burst patterns. (B) VF organoids at the same developmental stage exhibit a more segregated community structure. (Top) Network visualization demonstrating reduced inter-module connectivity compared to DF organoids. (Bottom) Module activity patterns show distinct temporal signatures with less correlation between different functional communities. (C) Module burst correlation distribution reveals fundamental architectural differences between DF (green) and VF (purple) organoids. DF modules display higher probability of correlated bursting. (D) Module burst timing variability distribution demonstrates that VF modules (purple) exhibit broader temporal spread compared to DF modules (green), which show a narrower, more synchronized timing profile. *p < 0.05, ***p < 0.0001, Kolmogorov-Smirnov test.
Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly

May 2025

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34 Reads

The mouse cortex is a canonical model for studying how functional neural networks emerge, yet it remains unclear which topological features arise from intrinsic cellular organization versus external regional cues. Mouse forebrain organoids provide a powerful system to investigate these intrinsic mechanisms. We generated dorsal (DF) and ventral (VF) forebrain organoids from mouse pluripotent stem cells and tracked their development using longitudinal electrophysiology. DF organoids showed progressively stronger network-wide correlations, while VF organoids developed more refined activity patterns, enhanced small-world topology, and increased modular organization. These differences emerged without extrinsic inputs and may be driven by the increased generation of Pvalb+ interneurons in VF organoids. Our findings demonstrate how variations in cellular composition influence the self-organization of neural circuits, establishing mouse forebrain organoids as a tractable platform to study how neuronal populations shape cortical network architecture.


1166 Optogenetic Modulation of Network Activity in Human Hippocampus

April 2025

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5 Reads

Neurosurgery

INTRODUCTION Seizures are made up of the coordinated activity of networks of neurons. It follows that control of neurons in the pathologic circuits of epilepsy could allow for control of the disease. In non-human disease models of epilepsy, optogenetics has been effective at stopping seizure-like activity by increasing inhibitory tone or decreasing excitation. However, this has not been shown in human brain tissue. Many of the genetic means for achieving channelrhodopsin expression in non-human models are not possible in humans, and vector-mediated methods are susceptible to species-specific tropism that may affect translational potential. There is currently no platform for testing the effects of these potentially disease-modifying tools on network activity in human brain tissue. METHODS Human hippocampus resected from patients with refractory epilepsy were collected, cut to 300um and plated at the air-fluid interface on cell-culture inserts. AAV transduction with channelrhodopsins driven by a glutamatergic promoter took place on the day of collection. Slices were plated on high-density micro-electrode arrays. Hyperactivity was promoted via bicuculline, low-magnesium media and kainic acid. Slices were illuminated by LED fiberoptics positioned over the slice using a custom recording chamber. RESULTS Neuronal transduction ranged from 12 – 54% (median 23%). Illumination of slices expressing the depolarizing channelrhodopsin HcKCR1 caused reduction in network firing rates in 8/8 slices expressing HcKCR1. Reductions in network firing rates were significant in all conditions, physiologic media, GABAaR blockade, low-magnesium media and low-magnesium media with kainic acid. CONCLUSIONS Here, we demonstrate AAV-mediated, optogenetic reductions in network firing rates of human hippocampal slices recorded on high-density microelectrode arrays under several hyperactivity provoking conditions. This platform can serve to bridge the gap between human and animal studies by exploring genetic interventions on network activity human brain tissue.


Fig. 3 | CARE IMPACT study workflow. a The CLIA gene panel ordered was either a Foundation Medicine gene panel (https://www.foundationmedicine.com/ portfolio) informed by the patient's diagnosis or a Stanford Solid Tumor Actionable Mutations Panel (STAMP)(https://stanfordlab.com/content/stanfordlab/en/ molecular-pathology/molecular-genetic-pathology.html/). All study components are described in the manuscript. b CARE IMPACT pipelines for identifying tumor vulnerabilities. CARE identifies gene expression outliers in each patient's tumor (hexagon) relative to all other tumors in a large compendium (pan-cancer analysis) and to a subset of the compendium restricted to tumors with similar RNA expression and/or histology (pan-disease analysis). For pan-disease analysis, the focus sample is compared to four cohorts to identify outliers. If an outlier is detected by at least two pan-disease cohorts, it is considered a consensus outlier. Fusion and RNA variant pipelines are also applied to identify expressed mutations and fusions.
Pathway support status of outliers detected by different comparative cohorts
Comparative analysis of RNA expression in a single institution cohort of pediatric cancer patients

March 2025

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25 Reads

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2 Citations

npj Precision Oncology

With the low incidence of mutations in pediatric cancers, alternate genomic approaches are needed to identify therapeutic targets. Our study, the Comparative Analysis of RNA Expression to Improve Pediatric and Young Adult Cancer Treatment, was conducted by the UC Santa Cruz Treehouse Childhood Cancer Initiative and Stanford University School of Medicine. RNA sequencing data from 33 children and young adults with a relapsed, refractory or rare cancer underwent CARE analysis to reveal activated cancer driver pathways and nominate treatments. We compare our pipeline to other gene expression outlier detection approaches and discuss challenges for clinical implementation. Of our 33 patients, 31 (94%) had findings of potential clinical significance. Findings were implemented in 5 patients, 3 of which had defined clinical benefit. We demonstrate that comparator cohort composition determines which outliers are detected. This study highlights the clinical utility and challenges of implementing comparative RNA sequencing analysis in the clinic.


Greater NOTCH2NL region pairwise homology matrix in T2T-CHM13
Genetic diversity and regulatory features of human-specific NOTCH2NL duplications

March 2025

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33 Reads

NOTCH2NL ( NOTCH2 -N-terminus-like) genes arose from incomplete, recent chromosome 1 segmental duplications implicated in human brain cortical expansion. Genetic characterization of these loci and their regulation is complicated by the fact they are embedded in large, nearly identical duplications that predispose to recurrent microdeletion syndromes. Using nearly complete long-read assemblies generated from 67 human and 12 ape haploid genomes, we show independent recurrent duplication among apes with functional copies emerging in humans ~2.1 million years ago. We distinguish NOTCH2NL paralogs present in every human haplotype ( NOTCH2NLA ) from copy number variable ones. We also characterize large-scale structural variation, including gene conversion, for 28% of haplotypes leading to a previously undescribed paralog, NOTCH2tv . Finally, we apply Fiber-seq and long-read transcript sequencing to human cortical neurospheres to characterize the regulatory landscape and find that the most fixed paralogs, NOTCH2 and NOTCH2NLA , harbor the greatest number of paralog-specific elements potentially driving their regulation.


HIPPIE: A Multimodal Deep Learning Model for Electrophysiological Classification of Neurons

March 2025

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2 Reads

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1 Citation

Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.


Figure 6: Conserved and divergent gene regulatory networks in ventral midbrain specification and maturation A. Activator eGRNs in the developing human midbrain organoids (D40-100) projected in a weighted UMAP based on co-expression and co-regulatory patterns. Nodes label hub TFs of eGRNs with number of target genes (color) and target regions (size) plotted, and edges label co-regulatory networks between eGRNs.
Figure S2 (related to Figure 1 and 2): Species-, individual-and cell type composition of midbrain progenitor pools and intra-and interspecies organoids
Interspecies Organoids Reveal Human-Specific Molecular Features of Dopaminergic Neuron Development and Vulnerability

November 2024

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73 Reads

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2 Citations

The disproportionate expansion of telencephalic structures during human evolution involved tradeoffs that imposed greater connectivity and metabolic demands on midbrain dopaminergic neurons. Despite the central role of dopaminergic neurons in human-enriched disorders, molecular specializations associated with human-specific features and vulnerabilities of the dopaminergic system remain unexplored. Here, we establish a phylogeny-in-a-dish approach to examine gene regulatory evolution by differentiating pools of human, chimpanzee, orangutan, and macaque pluripotent stem cells into ventral midbrain organoids capable of forming long-range projections, spontaneous activity, and dopamine release. We identify human-specific gene expression changes related to axonal transport of mitochondria and reactive oxygen species buffering and candidate cis- and trans -regulatory mechanisms underlying gene expression divergence. Our findings are consistent with a model of evolved neuroprotection in response to tradeoffs related to brain expansion and could contribute to the discovery of therapeutic targets and strategies for treating disorders involving the dopaminergic system.


Multimodal evaluation of network activity and optogenetic interventions in human hippocampal slices

November 2024

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262 Reads

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9 Citations

Nature Neuroscience

Seizures are made up of the coordinated activity of networks of neurons, suggesting that control of neurons in the pathologic circuits of epilepsy could allow for control of the disease. Optogenetics has been effective at stopping seizure-like activity in non-human disease models by increasing inhibitory tone or decreasing excitation, although this effect has not been shown in human brain tissue. Many of the genetic means for achieving channelrhodopsin expression in non-human models are not possible in humans, and vector-mediated methods are susceptible to species-specific tropism that may affect translational potential. Here we demonstrate adeno-associated virus–mediated, optogenetic reductions in network firing rates of human hippocampal slices recorded on high-density microelectrode arrays under several hyperactivity-provoking conditions. This platform can serve to bridge the gap between human and animal studies by exploring genetic interventions on network activity in human brain tissue.


Figure 1: Cloud-based electrophysiology data processing pipeline architecture. (A) Electrophysiology data from neuronal cultures is recorded on a local computer. Different neuronal cultures and their recordings are shown in Figures 4 and 7. (B) Once the dataset is saved, it is uploaded to a uniquely identified data bucket AWS S3 for permanent storage using the Uploader. An MQTT message is simultaneously sent to the job listener service to initiate data processing jobs. These jobs run containerized algorithms and are launched on the National Research Platform (NRP) computing cluster using Kubernetes. Results, including post-processed data and figures, are saved back to AWS S3. (C) The analysis outputs various interactive analytical figures for each dataset's network features and single-unit activity.
Figure 3: Minimum building block and job types. (A) The minimum pipeline building block utilizing dockerized algorithms and S3 data storage. Data and parameter settings are retrieved from S3, processed by containerized algorithms on NRP, and results are uploaded back to S3. (B) Users can save and load parameter settings to and from the S3 "service" bucket through the Dashboard. (C) Batch processing of numerous recordings is achieved by providing UUID and default parameter settings to the pipeline. Users can initiate this process through the local data uploader. (D) Chained jobs are implemented using a CSV job scheduler containing S3 data paths, job metadata, and parameter settings. Users can initiate job chaining from the online Dashboard.
Figure 5: Single neuron features from hourly recordings over days. (A) Spatial area of spiking activity in the mouse organoid on the HD-MEA over the recording time course. Color intensity corresponds to the amplitude of the neuron's action potential. (B) Changes in the Coefficient of Variation (CV) of interspike interval distribution over time. The bar plot shows the percentage of units with CV < 1 (red) and CV >= 1 (blue). (C) Distribution of the total number of single units for each day. (D) Average unit count with standard error of the mean (SEM) over time (Day 0: 16±2.58, Day 1: 17.45±1.55, Day 2: 25.25±1.03, Day 3: 23.64±1.18, Day 4: 24.04±1.49, Day 5: 23.22±1.12, Day 6: 22.29±0.81, Day 7: 25.28±1.20). (E) Single unit firing rate distribution over the 7 days. (F) Average firing rate (Hz) with SEM over time (Day 0: 2.33±0.35, Day 1: 2.74±0.16, Day 2: 2.7±0.13, Day 3: 3.19±0.14, Day 4: 3.11±0.16, Day 5: 3.27±0.15, Day 6: 3.35±0.16, Day 7: 3.49±0.22)
Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

November 2024

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36 Reads

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4 Citations

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. The vast amount of data generated requires efficient storage and sophisticated processing to extract neural function and network dynamics. However, analysis is often challenging due to the need for multiple software tools with different runtime dependencies. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage, complicating data management, sharing, and backup. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the analysis algorithms to serve as scalable and flexible building blocks within the pipeline. We designed graphical user interfaces and command line tools to remove the requirement of programming skills. The interactive visualizations provide multi-modality information on various neuronal features. This cloud-based pipeline is an efficient solution for electrophysiology data processing, the limitations of local software tools, and storage constraints. It simplifies the electrophysiology data analysis process and facilitates understanding neuronal activity. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings.


Comparative analysis of RNA expression identifies effective targeted drug in myoepithelial carcinoma

October 2024

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4 Reads

Myoepithelial carcinoma is an ultra-rare pediatric solid tumor with no targeted treatments. Clinical implementation of tumor RNA sequencing (RNA-Seq) for identifying therapeutic targets is underexplored in pediatric cancer. We previously published the Comparative Analysis of RNA Expression (CARE), a framework for incorporating RNA-Seq-derived gene expression into the clinic for difficult-to-treat pediatric cancers. Here, we discuss a 4-year-old male diagnosed with myoepithelial carcinoma who was treated at Stanford Medicine Children’s Health. A metastatic lung nodule from the patient underwent standard-of-care tumor DNA profiling and CARE analysis, wherein the patient’s tumor RNA-Seq profile was compared to over 11,000 uniformly analyzed tumor profiles from public data repositories. DNA profiling yielded no actionable mutations. CARE identified overexpression biomarkers and nominated a treatment that produced a durable clinical response. These findings underscore the utility of data sharing and concurrent analysis of large genomic datasets for clinical benefit, particularly for rare cancers with unknown biological drivers.


Citations (52)


... The outlier genes detected by CARE reflect activation of oncogenic pathways and are used to identify potentially valuable therapeutic targets. This method, including the impact of cohort selection on outlier detection, is described in a companion manuscript 25 . To illustrate the value of the CARE approach, we describe a young child with metastatic recurrence of an ultra-rare cancer, myoepithelial carcinoma, for which there were no known effective therapy options and no actionable mutations on tumor DNA profiling. ...

Reference:

Comparative analysis of RNA expression identifies effective targeted drug in myoepithelial carcinoma
Comparative analysis of RNA expression in a single institution cohort of pediatric cancer patients

npj Precision Oncology

... These channels exhibit high K⁺ conductance, enable membrane hyperpolarization. HcKCR1 provided inhibition with minimal phototoxicity and reduced post-illumination rebound [23]. Subsequent work identified another naturally-occurring KCR from Wobblia lunata, WiChR [24]; and engineered a highly sensitive HcKCR1 variant, HcKCR1-hs [25]; and mutant KCR variants with improved K + selectivity, e.g. ...

Multimodal evaluation of network activity and optogenetic interventions in human hippocampal slices

Nature Neuroscience

... We specifically chose cortical patterning due to the cortex's well-established role in adaptive information processing and its capability to encode, decode, and modify responses to novel inputs [49]. The organoids were interfaced with high-density microelectrode arrays (HD-MEAs) [50][51][52] (Fig. 2d-f), providing precise spatio-temporal control over the culture with a high number of putative neuronal units for potential computation. ...

Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

... Cultural biases in gamified content can alienate certain student groups, while data privacy concerns and excessive reliance on extrinsic rewards may have unintended consequences. Developing culturally sensitive content and ethical guidelines for gamification, including data protection measures and a focus on fostering intrinsic motivation, are necessary steps forward (Ly et al., 2024;Usachova, 2023). Teachers play a central role in the successful adoption of gamified learning, but teacher adaptation remains a challenge. ...

Gamifying Cell Culture Training: The 'Seru-Otchi' Experience for Undergraduates

Heliyon

... Concurrently, more emphasis is being put on the open sharing of large-scale neurophysiological data among researchers from the biological, cognitive, behavioral, and systems neuroscience fields, and machine learning and deep learning researchers [25,26,27]. In line with this, there are already various groups working on developing cloud-based OI platforms for collaboration, which will improve accessibility and may encourage such data sharing [28,29,30,31,32]. Cloud-based collaboration seems to be a solution where one lab tackles the biological aspects while others handle the engineering and computational tasks. ...

A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies

... Many researchers also refer to this as wetware since we grow these processing units in a dish with nutrients, which are primarily in the liquid form. 35 Culturing neural cells is essential to establish any bio-intelligence lab and requires both an appropriate workspace for neural culture 36 and suitable methods. ...

Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters

Cell Reports Methods

... Computational methods have become invaluable in identifying genetic factors linked to cancer. For instance, Sanders et al. used support vector machine (SVM) algorithms to highlight disruptions in RNA networks in cancer cell lines, providing critical insights for classification and precision treatment [18]. Compared to traditional methods, machine learning improves cancer classification accuracy. ...

Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors

Communications Biology

... Based on iPSCs' ability to self-organize and differentiate, they have developed innovative culture protocols for 3D brain organoids [3]. These techniques can effectively create complex structures similar to those found in a developing human brain, such as those of the pituitary gland [4,5], striatum [6], retina [7,8], and cortex [9]. ...

Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids

... Важливість цього програмного каркасу полягає у використанні апаратного забезпечення з відкритим вихідним кодом, який можна налаштувати відповідно до конкретних потреб різних застосувань. У статті [3] представлено архітектуру IР, розроблену для потреб наукової галузі клітинної біології. Продемонстровано її застосування у лабораторних експериментах, які стосувалися електрофізіології, мікроскопії та флюїдики. ...

IoT cloud laboratory: Internet of Things architecture for cellular biology
  • Citing Article
  • September 2022

Internet of Things

... • Cell line from stem cells: Embryonic stem cells (ESCs) and induced pluripotent stem cells (IPSCs) derived cultures offer several advantages including but not limited to: (1) reproducibility of cell types [96], (2) a large toolkit of genetically modified and disease cell lines https://www.wicell.org/ [97], (3) access to a wide range of cells derived from different species [39], and (4) ability to generate and differentiate into cell types from all regions of the brain [98]. Human neuronal differentiation can give insights through disease modeling (i.e., culture cells derived from sources/tissues within specific genetic signatures associated with a disease) [97] and may provide increased plasticity and function within SBI applications in vitro [18]. ...

Cerebral Organoids as an Experimental Platform for Human Neurogenomics