Biology subjects

Furuta, T.

Publications and source records attributed to Furuta, T..

3 recordsLinked to original sources

bioRxiv2022

FT-GO: a multiplex fluorescent tyramide signal amplification system for histochemical analysis

Tyramide signal amplification (TSA) is a highly sensitive method for histochemical analysis. Previously, we reported a TSA system, BT-GO, for bright-filed imaging. Here, we develop FT-GO (Fluorochromized Tyramide-Glucose Oxidase) as a multiplex fluorescent TSA system. FT-GO involves peroxidase-catalyzed deposition of FT with hydrogen peroxide produced in enzymatic reaction between glucose and glucose oxidase. We showed that FT-GO enhanced immunofluorescence signals while maintaining low background signals. Compared with indirect immunofluorescence detections, FT-GO demonstrated a more widespread distribution of monoaminergic projection systems in mouse and marmoset brains. For multiplex labeling with FT-GO, we quenched Ab-conjugated peroxidase using sodium azide. We applied FT-GO to multiplex fluorescent in situ hybridization, and succeeded in labeling neocortical interneuron subtypes by coupling with immunofluorescence. FT-GO immunofluorescence further increased the detectability of an adeno-associated virus tracer. Given its simplicity and a staining with a high signal-to-noise ratio, FT-GO would provide a versatile platform for histochemical analysis.

neuroscience↗

bioRxiv2022

GBScleanR: Robust genotyping error correction using hidden Markov model with error pattern recognition.

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Although the outstanding advantage of RRS in throughput, the obtained genotype data usually contains a large number of errors. Several error correction methods employing hidden Morkov model (HMM) have been developed to overcome these issues. Those methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum as compared to the existing tools in simulation datasets and achieves the most reliable genotype estimation in real data even with error prone markers.

bioinformatics↗

bioRxiv2021

Multi-Scale LM/EM Neuronal Imaging from Brain to Synapse with a Tissue Clearing Method, ScaleSF

The mammalian brain is organized over sizes that span several orders of magnitude, from synapses to the entire brain. Thus, a technique to visualize neural circuits across multiple spatial scales (multi-scale neuronal imaging) is vital for deciphering brain-wide connectivity. Here, we developed this technique by coupling successive light microscope/electron microscope (LM/EM) imaging with an ultrastructurally-preserved tissue clearing method, ScaleSF. Our multi-scale neuronal imaging incorporates 1) brain-wide macroscopic observation, 2) mesoscopic circuit mapping, 3) microscopic subcellular imaging, and 4) EM imaging of nanoscopic structures, allowing seamless integration of structural information from the brain to synapses. We applied the technique to three neural circuits of two different species, mouse striatofugal, mouse callosal, and marmoset corticostriatal projection systems, and succeeded in the simultaneous interrogation of their circuit structure and synaptic connectivity in a targeted way. Our multi-scale neuronal imaging will significantly advance the understanding of brain-wide connectivity by expanding the scales of objects.

neuroscience↗