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. 2020 Jul 1;31(14):1512-1524.
doi: 10.1091/mbc.E20-04-0269. Epub 2020 May 13.

Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments

Diego Morone  1   2 , Alessandro Marazza  1   2   3 , Timothy J Bergmann  1   2 , Maurizio Molinari  1   2   4
Affiliations

Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments

Diego Morone et al. Mol Biol Cell. .

Abstract

Endolysosomal compartments maintain cellular fitness by clearing dysfunctional organelles and proteins from cells. Modulation of their activity offers therapeutic opportunities. Quantification of cargo delivery to and/or accumulation within endolysosomes is instrumental for characterizing lysosome-driven pathways at the molecular level and monitoring consequences of genetic or environmental modifications. Here we introduce LysoQuant, a deep learning approach for segmentation and classification of fluorescence images capturing cargo delivery within endolysosomes for clearance. LysoQuant is trained for unbiased and rapid recognition with human-level accuracy, and the pipeline informs on a series of quantitative parameters such as endolysosome number, size, shape, position within cells, and occupancy, which report on activity of lysosome-driven pathways. In our selected examples, LysoQuant successfully determines the magnitude of mechanistically distinct catabolic pathways that ensure lysosomal clearance of a model organelle, the endoplasmic reticulum, and of a model protein, polymerogenic ATZ. It does so with accuracy and velocity compatible with those of high-throughput analyses.

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Figures

FIGURE 1:
FIGURE 1:
Standard segmentation approaches. (A) CLSM image where LAMP1 decorates the limiting membrane of EL and its inset. (B) Automatic IsoData thresholding followed by watershed segmentation; (C) random forest machine learning with iLastik; (D) Support Vector Machine (SVM); (E) trainable Weka segmentation; (F) Squassh region competition segmentation; (G) Icy spot detection plugin; (H) cmeAnalysis; (I) Interaction Analysis ImageJ plugin; (J) Colocalisation Pipeline ImageJ plugin; (K) circular Hough transform (CHT); (L) Stardist star-convex polygons ImageJ plugin. In all images, true positives are in white, false negatives in yellow, and false positives in blue with respect to manual segmentation. F1 score for segmentation and Intersection over Union (IoU) are indicated.
FIGURE 2:
FIGURE 2:
Analysis workflow and deep learning architecture. (A) Analysis workflow. (a) Multichannel CLSM image. LAMP1 decorates the limiting membrane of EL. SEC62 stains the cargo. (b) Cells to be analyzed are identified as regions of interest (ROIs). (c) Signal outside ROIs is cleared and image is converted to RGB color image. (d) This RGB is then normalized in the range [0, 1] and rescaled to a pixel size of 0.025 µm. (e, f) Image is segmented into two classes: empty (cyan) and loaded EL (magenta). Classes are filtered for a configurable minimum size, which in our case was equal to the minimum of all annotated EL (dotted line, n = 1573). Diameter scale was also added as a reference. (g–i) Total number of EL for each class and each ROI is listed with a configurable number of individual EL parameters (e.g., average size, fluorescence intensity, circularity). (B) Deep learning architecture is a seven–resolution level 2D U-net fully convolutional network with 16 base feature channels that takes RGB images as input. Green channel shows the EL structure, red channel the protein or the ER subdomain delivered within EL.
FIGURE 3:
FIGURE 3:
Computational performance of the machine learning architecture. (A) Fully annotated and segmented image of wild-type mouse embryonic fibroblasts (MEF) transfected with ATZ-HA (red). LAMP1-positive EL are stained in green. The annotated RGB and its inset show empty EL (cyan ROIs) and ATZ-loaded EL (magenta ROIs). The segmented image and its inset show empty EL (cyan) and ATZ-loaded EL (magenta). False positives (not annotated but segmented) are in blue. False negatives,(annotated but not segmented) are in yellow. (B) Single channels of input image, inset of LAMP1 channel, and its overlay with annotated ROIs. Scale bars 10 µm. Computational performance is evaluated with different metrics. After 3 × 105 iterations, (C) IoU is 0.881 ± 0.012 and 0.877 ± 0.014 for empty and cargo-loaded EL classes, respectively (average ± SD of three validation images). (D) ROC curves for both classes show AUCs of 99.43% and 99.47% for empty and loaded EL, respectively. (E) F1 scores for segmentation task 0.752 ± 0.134 and 0.814 ± 0.100, respectively. (F) F1 scores for detection task 0.777 ± 0.136 and 0.790 ± 0.055, respectively.
FIGURE 4:
FIGURE 4:
Mimicking ER delivery within EL during recov-ER-phagy. (A) CLSM shows MEF transfected transiently with SEC62-HA and (B) with SEC62LIR-HA, subsequently segmented and quantified with LysoQuant. (C) Quantification of the same set of images by three different operators (with the Lab mean) and by LysoQuant to establish the fraction of EL containing SEC62-labeled ER in both SEC62- and SEC62LIR-expressing MEF cells. (D) Same as C to compare the time required for manual and LysoQuant-operated detection and segmentation tasks.
FIGURE 5:
FIGURE 5:
Quantification of misfolded protein delivery to lysosomes. (A) CLSM shows delivery of ATZ within LAMP1-positive EL in wild-type MEF. (B) Same as A in STX17KO MEF. (C) Quantification of ATZ delivery within EL in both wild-type and STX17KO MEF. (D) Time required for manual and LysoQuant-operated detection and segmentation tasks.
FIGURE 6:
FIGURE 6:
Quantification of ER remodeling in HEK293 cells. (A) CLSM showing HEK293 cotransfected transiently with the late endosomal/lysosomal marker GFP-Rab7 and FAM134-HA. (B) Same as A in cells transfected for expression of FAM134BLIR-HA. Nuclei are shown to identify transfected and nontransfected HEK293 cells. (C) Same as 4C to quantify ER delivery within GFP-RAB7-positive EL in cells expressing FAM134B and FAM134BLIR, respectively. (D) Same as 4D.
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