skip to main content


Title: A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images
Measuring the organization of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. Different approaches have been developed to quantify these structures, typically based on fiber segmentation or on matrix representation and transformation of the image, each with its own advantages and disadvantages. Here we present AFT − Alignment by Fourier Transform , a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighborhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. This approach can thus easily accommodate different image resolutions and biological systems. 2) The length scale of decay in alignment can be extracted by comparing neighborhood sizes, revealing the overall distance that features remain anisotropic. 3) The approach is ambivalent to the signal source, thus making it applicable for a wide range of imaging modalities and is dependent on fewer input parameters than segmentation methods. 4) Finally, compared to segmentation methods, this algorithm is computationally inexpensive, as high-resolution images can be evaluated in less than a second on a standard desktop computer. This makes it feasible to screen numerous experimental perturbations or examine large images over long length scales. Implementation is made available in both MATLAB and Python for wider accessibility, with example datasets for single images and batch processing. Additionally, we include an approach to automatically search parameters for optimum window and neighborhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.  more » « less
Award ID(s):
2000554
NSF-PAR ID:
10316487
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Frontiers in Computer Science
Volume:
3
ISSN:
2624-9898
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies ( e.g. , single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images. 
    more » « less
  2. Various biomacromolecule components of extracellular matrix (ECM) link together to form a structurally stable composite. Monitoring of such matrix microstructure can be very important in studying structure-associated cellular processes, improving cellular function, and ensuring sufficient mechanical integrity in engineered tissues. This paper describes a novel method to study microscale alignment of matrix in engineered tissue scaffolds (ETS) that were usually composed of a variety of biomacromolecules derived by cells. as the organization of overall biomacromolecule network has been seldomly examined. First, a trained loading function was derived from Raman spectra of highly aligned native tissue via PCA, where prominent changes associated with Raman bands (e.g., 1444, 1465, 1605, 1627-1660 and 1665-1689 cm−1) were detected with respect to the polarized angle. These changes were mainly caused by the aligned matrix of many compounds within the tissue relative to the laser polarization, including proteins, lipids and carbohydrates. Hence this trained function was applied to quantify the alignment within ETS of various matrix components derived by cells. A simple metric called Amplitude Alignment Metric was derived to correlate the orientation dependence of polarized Raman spectra of ETS to the degree of matrix alignment. By acquiring polarized Raman spectra of ETS at micrometer regions, the Amplitude Alignment Metric was significantly higher in anisotropic ETS than isotropic ones. The PRS method showed a lower p-value for distinguishing the alignment between the two types of ETS as compared to the microscopic method for detecting fluorescently labeled protein matrices at similar microscopic scale. These results indicate the anisotropy of complex matrix in engineered tissue can be assessed at microscopic scale using a PRS-based simple metric, superior to traditional microscopic method. This PRS-based method can serve as a complementary tool for the design and assessment of engineered tissues that mimic the native matrix organizational microstructures. 
    more » « less
  3.  
    more » « less
  4. Abstract

    Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype. However, there is an unexplored dimensional scale window of three-dimensional (3D) substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell’s operating length scales (10–100 μm). This paper demonstrates the fabrication of such high-fidelity fibrous substrates using a melt electrowriting (MEW) technique. This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures. Using fibroblasts as a model cell system, the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality (2D vs. 3D) and porous microarchitecture (randomly oriented, “non-woven” vs. precision-stacked, “woven”). Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured. The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes. The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates. The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing—metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.

     
    more » « less
  5. ABSTRACT BACKGROUND AND PURPOSE

    Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre‐ and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image‐processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.

    METHODS

    We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two‐part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single‐subject level, which specifically identifies the disconnections associated with focal white matter damage.

    RESULTS

    The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long‐range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor‐based registration, which aligns images using an approach sensitive to white matter microstructure.

    CONCLUSIONS

    Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large‐scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

     
    more » « less