Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods

Kistenev, Yury V.; Vrazhnov, Denis A.; Nikolaev, Viktor V.; Sandykova, Ekaterina A.; Krivova, N. A.

doi:10.1134/s0006297919140074

Cited by 28 publications

(19 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given this knowledge, the relevance of MPM as an adjunct to histopathological or clinical frameworks sets a novel precedent for enhanced deceased donor kidney assessment. The ability of MPM to facilitate automated evaluation of the degree of renal interstitial brosis heralds the prospects of arti cial intelligence within the context of histopathology evaluation 19 Ongoing research to establish the utility of MPM and arti cial intelligence in assessing the other components of Remuzzi scoring is underway 20 .…”

Section: Discussionmentioning

confidence: 99%

Multi-Photon Microscopy for the Evaluation of Interstitial Fibrosis in Extended Criteria Donor Kidneys: A Proof-of-Concept Study

Teo

Ooi

et al. 2021

Preprint

View full text Add to dashboard Cite

Introduction:This study evaluated the initial use of label-free second harmonic generation (SHG) imaging with two-photon excitation (2PE) auto-fluorescence for the quantification of collagen/fibrosis on pre-implantation biopsies of extended criteria donors (ECD).Methods:A total of 20 core ECD kidney transplant biopsy specimen tissues from 7 ECD donors, taken at the time of pre-implantation were retrieved, cut into 5-micron sections, mounted on slides and deparaffinized. The core needle biopsies were imaged with 2X and 20X objective using the commercially available laser-based Genesis® 200 (Histoindex Pte Ltd and Clinnovate Pte Ltd, Singapore). The entire core was selected as the Region of Interest (ROI). Corresponding clinical information from transplant donors were retrieved. Histopathological review was performed, and all biopsies had Interstitial Fibrosis (IF)/Tubular Atrophy (TA) scores > 0. Collagen parameters measured included quantification by the Collagen Area Ratio in Tissue (CART) and qualitative measurements by Collagen Reticulation Index (CRI).Results: 20 explant core biopsies were extracted from 10 donor kidney samples, of which originated from 7 donors. Table 1 depicts the baseline ECD characteristics of the donors. For the kidneys with multiple biopsies done, we obtained an average score of the collagen parameters across the different samples. Biopsies classified with > 85% KDPI score had significantly higher CAR and CART than biopsies with ≤ 85% KDPI score.Conclusion:MPM is an evolving technology that enables the quantification of the amount (CART) and quality (CRI) of collagen deposition in unstained explant biopsies of ECD kidneys. This initial evaluation found significant differences in both parameters between ECD kidneys with more or less than 85% KDPI scores.

show abstract

Section: Discussionmentioning

confidence: 99%

Multi-Photon Microscopy for the Evaluation of Interstitial Fibrosis in Extended Criteria Donor Kidneys: A Proof-of-Concept Study

Teo

Ooi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Microscopy is one of the most popular methods for TE (Dhulekar et al, 2016;Buggenthin et al, 2017;Liang et al, 2017;Brent and Boucheron, 2018;Christiansen et al, 2018;Nitta et al, 2018;Rivenson et al, 2019;Vu et al, 2019), and visual images are also demonstrated to be useful (Gholami et al, 2018). Non-linear imaging methods are also actively developed, including multiphoton (Kistenev et al, 2019) and second harmonic generation (SHG) microscopy, allowing for the visualization of tissue structure and permitting imaging of samples without labeling (Hanson et al, 2013). Xray microCT, utilized for additive manufacturing (Du Plessis et al, 2018), was demonstrated to be a viable technique for 3D histology (Katsamenis et al, 2019).…”

Section: Imaging Data Retrieval and Analysismentioning

confidence: 99%

“…Pattern discovery can be grouped into (i) ML methods directly targeting imaging data (Brent and Boucheron, 2018;Casiraghi et al, 2018;Gupta et al, 2019;Kistenev et al, 2019;Li et al, 2019;Rivenson et al, 2019;Vu et al, 2019), (ii) ML-based predictive modeling for TE scaffolds (Buggenthin et al, 2017;Tanaka et al, 2017;Chaudhury et al, 2018;Nitta et al, 2018;Marzi et al, 2019;Waisman et al, 2019), and (iii) a broad range of bioinformatics such as network analysis (Camacho et al, 2018). Specifically, several studies are (i) predicting tissue properties with DL from images or experimental observations (Liang et al, 2017;Brent and Boucheron, 2018;Kusumoto et al, 2018;Berisha et al, 2019;Gupta et al, 2019;Kistenev et al, 2019;Lutnick et al, 2019;Rivenson et al, 2019;Vu et al, 2019;Xie et al, 2019), (ii) classifying tissue type, state, and material properties with various ML methods (Casiraghi et al, 2018;Hailstone et al, 2018;Li et al, 2019), (iii) integrating multiple imaging platforms and experiments (Heredia-Juesas et al, 2018), (iv) modeling tissues for pattern discovery and predictive modeling (Bilgin et al, 2010;Yener, 2016;Kusumoto et al, 2018), and (v) extracting information from images for TE (Gholami et al, 2018).…”

Section: Pattern Discovery and Translation To A Blueprint For 3dbpmentioning

confidence: 99%

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Kim

McKee

Fontenot

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.

show abstract

“…Machine learning approaches have been used to classify collagen in space. 13 However, previous studies generate models to give a diagnosis as an output for healthy or unhealthy collagen. This requires a higher composition level of collagens as fibrils and higher observation dimensions around 10 μm.…”

Section: Introductionmentioning

confidence: 99%

Method for measurement of collagen monomer orientation in fluorescence microscopy

et al. 2021

View full text Add to dashboard Cite

Significance: Collagen is the most abundant protein in vertebrates and is found in tissues that regularly experience tension, compression, and shear forces. However, the underlying mechanism of collagen fibril formation and remodeling is poorly understood.Aim: We explore how a collagen monomer is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the monomer has a weak contrast and is relatively small. It is possible to attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy.Approach: We present two methods for detecting a monomer and classifying its orientation. A modified Gabor filter set and an automatic classifier trained by convolutional neural network based on a synthetic dataset were used.Results: By evaluating the performance of these two approaches with synthetic and experimental data, our results show that it is possible to determine the location and orientation with an error of ∼37 deg of a single monomer with fluorescence microscopy.Conclusions: These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling.

show abstract

Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods

Cited by 28 publications

References 60 publications

Multi-Photon Microscopy for the Evaluation of Interstitial Fibrosis in Extended Criteria Donor Kidneys: A Proof-of-Concept Study

Multi-Photon Microscopy for the Evaluation of Interstitial Fibrosis in Extended Criteria Donor Kidneys: A Proof-of-Concept Study

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Method for measurement of collagen monomer orientation in fluorescence microscopy

Contact Info

Product

Resources

About