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Researchers in behavioral neuroscience have long sought imaging techniques that can identify and distinguish neural ensembles that are activated by sequentially applied stimuli at single-cell resolution across the whole brain. Taking advantage of the different kinetics of immediate-early genes' mRNA and protein expression, we addressed this problem by developing tyramide-amplified immunohistochemistry-fluorescence in situ hybridization (TAI-FISH), a dual-epoch neural-activity-dependent labeling protocol. Here we describe the step-by-step procedures for TAI-FISH on brain sections from mice that were sequentially stimulated by morphine (appetitive first stimulus) and foot shock (aversive second stimulus). We exemplify our approach by FISH-labeling the neural ensembles that were activated by the second stimulus for the mRNA expression of c-fos, a well-established marker of neural activation. We labeled neuronal ensembles activated by the first stimulus using fluorescence immunohistochemistry (IHC) for the c-fos protein. To further improve the temporal separation of the c-fos mRNA and protein signals, we provide instructions on how to enhance the protein signals using tyramide signal amplification (TSA). Compared with other dual-epoch labeling techniques, TAI-FISH provides better temporal separation of the activated neural ensembles and is better suited to investigation of whole-brain responses. TAI-FISH has been used to investigate neural activation patterns in response to appetitive and aversive stimuli, and we expect it to be more broadly utilized for visualizing brain responses to other types of stimuli, such as sensory stimuli or psychiatric drugs. From first stimulation to image analysis, TAI-FISH takes ∼9 d to complete.
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http://dx.doi.org/10.1038/nprot.2017.134 | DOI Listing |
Phys Rev Lett
August 2025
Southern University of Science and Technology, Department of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Shenzhen 518055, China.
Quantum computing is expected to provide an exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as "quantum data embedding," to maximize classification performance remains a critical challenge. In this Letter, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1).
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
View Article and Find Full Text PDFJ Neurosci
September 2025
Center for Studies in Behavioural Neurobiology, Department of Psychology, Concordia University, Montreal, QC, Canada, H4B 1R6
Adaptive behavior depends on a dynamic balance between acquisition and extinction memories. Male and female rodents differ in extinction learning rates, suggestion potential sex-based differences in this balance. In males, deletion of extinction-recruited neurons in the central nucleus (CN) of the amygdala impairs extinction retrieval, shifting behavior toward acquisition (Lay et al.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
College of Agriculture and Biological Science, Dali University, Dali 671000, China.
The E76K mutation in protein tyrosine phosphatase (PTP) SHP2 is a recurrent driver of developmental disorders and cancers, yet the mechanism by which this single-site substitution promotes persistent activation remains elusive. Here, we combine path-based conformational sampling, unbiased molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) to elucidate how E76K reshapes the activation landscape and regulatory architecture of SHP2. Using a minimum-action trajectory derived from experimentally determined closed and open structures, we generated representative transition intermediates to guide the unbiased MD simulations.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, 5735 S. Ellis Ave., SCL 123, Chicago, Illinois 60637, USA.
Molecular dynamics simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity.
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