Dataset Information

Uncovering microstructural architecture from histology.

ABSTRACT: Microstructural tissue organization underlies the complex connectivity of the brain and controls properties of connective, muscle, and epithelial tissue. However, discerning microstructural architecture with high resolution for large fields of view remains prohibitive. We address this challenge with computational scattered light imaging (ComSLI), which exploits the anisotropic light scattering of aligned structures. Using a rotating lightsource and a high-resolution camera, ComSLI determines fiber architecture with micrometer resolution from histological sections across preparation and staining protocols. We show complex fiber architecture in brain and non-brain sections, including histological paraffin-embedded sections with various stains, and demonstrate its applicability on animal and human tissue, including disease cases with altered microstructure. ComSLI opens new avenues for investigating fiber architecture in new and archived sections across organisms, tissues, and diseases.

SUBMITTER: Georgiadis M

PROVIDER: S-EPMC10996646 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Micron-resolution fiber mapping in histology independent of sample preparation.

Georgiadis Marios M Auf der Heiden Franca F Abbasi Hamed H Ettema Loes L Nirschl Jeffrey J Moein Taghavi Hossein H Wakatsuki Moe M Liu Andy A Ho William Hai Dang WHD Carlson Mackenzie M Doukas Michail M Koppes Sjors A SA Keereweer Stijn S Sobel Raymond A RA Setsompop Kawin K Liao Congyu C Amunts Katrin K Axer Markus M Zeineh Michael M Menzel Miriam M

bioRxiv : the preprint server for biology 20250918

Mapping the brain's fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Here, we show that computational scattered light imaging (ComSLI) can map fiber networks in histology independent of sample preparation, also in formalin-fixed paraffin-embedded (FFPE) tissues including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different sample pr ...[more]

PMID: 38585744

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Project description:Background and purposeTraumatic brain injury is a major public health problem worldwide. Accurately evaluating the brain microstructural changes in traumatic brain injury is crucial for the treatment and prognosis assessment. This study aimed to assess the longitudinal brain microstructural changes in traumatic brain injury in the rat using diffusional kurtosis imaging.Materials and methodsDiffusional kurtosis imaging was performed in a group of 5 rats at preinjury and 3, 14, and 28 days after traumatic brain injury. The diffusional kurtosis imaging parameters were measured in the bilateral cortex, hippocampus, and corpus callosum. Another 4 groups of 5 rats were used in brain immunohistochemistry analysis of neuron (neuron-specific nuclear protein [NeuN]), astroglia (glial fibrillary acidic protein [GFAP]), microglia (ionized calcium binding adaptor molecule 1 [Iba-1]), and myelin (myelin basic protein [MBP]) in the same area as the diffusional kurtosis imaging parameter measurements. Furthermore, 2 groups of 6 rats underwent a Morris water maze test at 28 days after traumatic brain injury. The diffusional kurtosis imaging parameters, immunohistochemistry results, and Morris water maze test results were compared longitudinally or between traumatic brain injury and control groups.ResultsCompared with baseline, traumatic brain injury in the rat showed higher mean kurtosis and mean diffusivity values in the ipsilateral perilesional cortex and hippocampus and lower fractional anisotropy values in the corpus callosum (P < .05). The traumatic brain injury group showed higher staining of GFAP and Iba-1 and lower immunohistochemistry staining of NeuN and MBP in all ipsilateral ROIs (P < .05). There was no significant difference in the contralateral ROIs in diffusional kurtosis imaging parameters or immunohistochemistry results. The Morris water maze test revealed lower platform crossing times in the probe test (P < .05).ConclusionsOur study indicated that there were longitudinal changes in diffusional kurtosis imaging parameters, accompanied by multiple pathologic changes at different time points following traumatic brain injury, and that mean kurtosis is more sensitive to detect microstructural changes, especially in gray matter, than mean diffusivity and fractional anisotropy.

Project description:BackgroundThe number of panicles per plant, number of grains per panicle, and 1000-grain weight are important factors contributing to the grain yield per plant in rice. The Rice Diversity Panel 1 (RDP1) contains a total of 421 purified, homozygous rice accessions representing diverse genetic variations within O. sativa. The release of High-Density Rice Array (HDRA, 700 k SNPs) dataset provides a new opportunity to discover the genetic variants of panicle architectures in rice.ResultsIn this report, a new method genome-phenome wide association study (GPWAS) was performed with 391 individuals and 27 traits derived from RDP1 to scan the relationship between the genes and multi-traits. A total of 1985 gene models were linked to phenomic variation with a p-value cutoff of 4.49E-18. Besides, 406 accessions derived from RDP1 with 411,066 SNPs were used to identify QTLs associated with the total spikelets number per panicle (TSNP), grain number per panicle (GNP), empty grain number per panicle (EGNP), primary branch number (PBN), panicle length (PL), and panicle number per plant (PN) by GLM, MLM, FarmCPU, and BLINK models for genome-wide association study (GWAS) analyses. A total of 18, 21, 18, 17, 15, and 17 QTLs were identified tightly linked with TSNP, GNP, EGNP, PBN, PL, and PN, respectively. Then, a total of 23 candidate genes were mapped simultaneously using both GWAS and GPWAS methods, composed of 6, 4, 5, 4, and 4 for TSNP, GNP, EGNP, PBN, and PL. Notably, one overlapped gene (Os01g0140100) were further investigated based on the haplotype and gene expression profile, indicating this gene might regulate the TSNP or panicle architecture in rice.ConclusionsNearly 30 % (30/106) QTLs co-located with the previous published genes or QTLs, indicating the power of GWAS. Besides, GPWAS is a new method to discover the relationship between genes and traits, especially the pleiotropy genes. Through comparing the results from GWAS and GPWAS, we identified 23 candidate genes related to panicle architectures in rice. This comprehensive study provides new insights into the genetic basis controlling panicle architectures in rice, which lays a foundation in rice improvement.

Dataset Information

Uncovering microstructural architecture from histology.

Publications

Micron-resolution fiber mapping in histology independent of sample preparation.

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