High-content high-resolution microscopy and deep learning-assisted analysis reveals host and bacterial heterogeneity during Shigella infection
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, United Kingdom
Figures
Figure 1 with 1 supplement
High-content imaging captures the heterogeneity of S. flexneri infection.
(A) Representative high-resolution microscopy image of HeLa cells infected with S. flexneri expressing mCherry acquired with the high-content microscope ZEISS CellDiscoverer 7. Scale bar, 10 µm. (B) Enlarged view of insets in A, highlighting a S. flexneri bacteria polymerising an actin tail (1) and entrapped in a septin cage (2), scale bar, 1 µm. (C) Percentage of infected cells at 3 hpi of 7 independent experiments. Graph represents mean = 11.49% ± SD=4.36%. (D) Percentage of infected cells harbouring different bacterial doses, graph represents mean ± SD. (E) Distance of the bacteria to the centroid of the host cell. Graph represents the median (8.74 µm) and interquartile range (6.51–11.05 µm). Values from n=4990 cells from 7 independent experiments. (F) Distance of individual bacteria to the centroid of the host cell depending on its bacterial load. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test. (G) Area of cells depending on infection. Graph represents the median area (uninfected: 3031 µm2, infected: 4148 µm2) and interquartile range (uninfected: 2308–4169 µm2, infected: 3062–5752 µm2). Values from n=9469 uninfected cells and n=1031 infected cells from 7 independent experiments. (H) Area of infected cells depending on their bacterial load. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test. (I) Nuclear area of cells depending on infection. Graph represents the median area (uninfected: 1551 µm2, infected: 1897 µm2) and interquartile range (uninfected: 1209–1950 µm2, infected: 1486–2333 µm2). Values from n=9469 uninfected cells and n=1031 infected cells from 7 independent experiments. (J) Nuclear area of infected cells depending on its bacterial load. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test.
Figure 1—figure supplement 1
S.flexneri infection induces host cell size changes.
(A) Representative images used as input for CellProfiler analysis (top) and their corresponding segmentations (bottom). Host cell boundaries were identified using a propagation method from the identified nucleus based on Otsu thresholding. (B) Hoescht fluorescence intensity of sum-projected nuclei as proxy of nuclear volume. Graph represents normalised median (uninfected: 1, infected: 1.294) and interquartile range (uninfected: 0.7588–1.287, infected: 0.9534–1.878) of n=2071 uninfected cells and n=195 infected cells from 3 independent experiments. Mann-Whitney U test. (C–D) Cell and nuclear area in presence and absence of infection and latrunculin B. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test.
Figure 2 with 1 supplement
S. flexneri infection reduces host DNA synthesis and host protein synthesis.
(A) Representative microscopy image of HeLa cells infected with S. flexneri expressing mCherry at 3 hpi after 1 hr incubation with Click-iT EdU. Asterisks label the nucleus of infected cells with reduced EdU incorporation. Contour of selected cells is delimited with a dashed line. Scale bar, 10 µm. (B) EdU acquisition in uninfected cells is bimodal. Graph represents the normalised median of the ratio between the fluorescence intensity of EdU and Hoechst (1), and interquartile range (0.5924–31.22). Values from n=8941 cells from 3 independent experiments. An arbitrary threshold of 5 is selected to define cells in S-phase. (C) Frequency of cells in S-phase (as defined in B) depending on infection. Graph represents mean ± SD (uninfected: 34.45% ± 9.78%, infected: 35.89% ± 4.69%). Student’s t-test. (D) EdU acquisition in cells in S-phase depending on infection. Graph represents the normalised median of the ratio between the fluorescence intensity of EdU and Hoechst (uninfected: 75.14, infected: 44.73), and interquartile range (uninfected: 27.89–131.0, infected: 22.05–93.36), from 3065 uninfected and 552 infected cells. Mann-Whitney U test. (E) EdU acquisition in cells in S-phase depending on bacterial load. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test. (F) Representative microscopy image of HeLa cells infected with S. flexneri expressing mCherry at 3 hpi after 1 hr incubation with Click-iT AHA. Asterisks label the infected cells with reduced AHA incorporation. Scale bar, 10 µm. (G). AHA acquisition in host cells depending on infection. Graph represents the normalised median of AHA fluorescence intensity (uninfected: 1, infected: 0.86), and interquartile range (uninfected: 0.7850–2.694, infected: 0.6207–1.241), from 36 uninfected and 233 infected cells. Mann-Whitney U test. (H) AHA acquisition in infected cells depending on bacterial load. Graph represents the median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test.
Figure 2—figure supplement 1
Click-iT EdU and Click-iT AHA specifically label de novo DNA and protein synthesis in HeLa cells.
(A) Representative widefield image of HeLa cells after Click-iT EdU reaction in the presence or absence of aphidicolin to inhibit eukaryotic DNA replication. Scale bar, 10 µm. (B) Quantification of de novo DNA synthesis in single HeLa cells in A as the ratio of nuclear Click-iT EdU signal to Hoechst. Graph represents the normalised median (untreated: 1, aphidicolin: 0.7667) and interquartile range (untreated: 0.8474–6.571, aphidicolin: 0.6816–0.8543) from 15,828 untreated and 14,943 treated cells from 3 independent experiments. Mann-Whitney U test. (C) Representative Airyscan confocal images of HeLa cells after Click-iT AHA reaction in the presence or absence of cycloheximide to block eukaryotic translational elongation. Scale bar, 10 µm. (D) Quantification of cytoplasmic Click-iT AHA signal of the HeLa cells in A. Graph represents normalised median (untreated: 1, cycloheximide: 0.3531) and interquartile range (untreated:0.6794–1.401, cycloheximide: 0.2504–0.6637) of n=268 untreated and n=258 cycloheximide treated cells from 3 independent experiments. Mann-Whitney U test.
Figure 3
Microscopy pipeline to automatically identify SEPT7-S. flexneri interactions.
(A) Diagram representing the steps involved in the imaging and analysis pipeline. After infection, fixation, and staining, a high number of images are automatically acquired using the high-content microscope. Scale bar, 10 µm. These are then processed to segment bacteria based on their fluorescence, so that a square field containing the bacteria in the centre is cropped and saved for subsequent analysis. A deep learning model based on convolutional neural network (CNN) is applied using Jupyter notebooks and the Python Keras library hosted on Colab to sort individual, isolated bacteria. Several processing steps are applied using Python to remove noise (band-pass filtering by difference of Gaussian and mean filters), normalise the data across datasets for comparison, and remove signal away from the bacteria which is irrelevant for the identification of SEPT7 assemblies. Scale bar, 1 µm. Finally, a similar second deep learning model based on CNN is applied to identify SEPT7 interacting with bacteria.
Figure 4 with 1 supplement
Training of a deep learning algorithm to identify single isolated bacteria cells and SEPT7-associated S. flexneri.
(A) Table summarising the annotated dataset used for the training process. The labels of the two classification classes used were ‘Single bacteria’ and ‘Clump’. The annotated images were randomly split into two groups, one used as training dataset (comprising 80% of the images) and another used as validation dataset (with the remaining 20%). A separate annotated dataset was used for testing. (B) Representative Airyscan images of bacteria used for each class. 2D max projections of z-stacks were used for training. Scale bar, 1 µm. (C) Architecture of the deep learning algorithm used for training. Each row describes the characteristics of the sequential transformation steps applied. Conv2D stands for 2D Convolution. (D, E) Accuracy and Loss as metrics to represent the training process over subsequent epochs (entire passing of the training data through the algorithm). Accuracy increases and loss decreases for both training and validation datasets, indicating a good fit of the model to the data. Vertical dashed grey line indicates Early Stopping, or epoch value for the minimum validation Loss. (F) Confusion matrix performed on an annotated test dataset not used previously for the training or validation process, indicating the percentage of predictions that were correct or wrong for each class. (G) Precision, recall, and F1 score as metrics that summarise the performance of the model or classifier. (H) Table summarising the annotated dataset used for the training process. The labels of the two classification classes used were ‘Septin’ and ‘Negative’. The annotated images were randomly split into three groups, one used as training dataset (comprising 75% of the images), another used as validation dataset (with 15%), and a last one used as test dataset (with 10%). Due to the data being very imbalanced (15% natural frequency of SEPT7-associated bacteria), the images comprised in the Negative class were under-sampled as indicated in the table. (I) Representative Airyscan images of bacteria used for each class. 2D max projections of z-stacks were used for training. Scale bar, 1 µm. (J) Architecture of the deep learning algorithm used for training. Each row describes the characteristics of the sequential transformation steps applied. Conv2D stands for 2D Convolution, Batch_norm stands for Batch normalisation. (K, L) Accuracy and Loss as metrics to represent the training process over subsequent epochs. Accuracy increases and loss decreases for both training and validation datasets, indicating a good fit of the model to the data. Vertical dashed grey line indicates Early Stopping or epoch value for the minimum validation Loss. (M) Confusion matrix performed on the test dataset not used previously for the training or validation process, indicating the percentage of predictions that were correct or wrong for each class. (N) Precision, recall, and F1 score as metrics that summarise the performance of the model or classifier.
Figure 4—figure supplement 1
Additional examples of the annotated datasets for deep learning.
(A) Representative images of multiple bacteria associated with SEPT7. Scale bar, 1 µm. (B) Representative images of the training and validation dataset to identify single bacteria vs clumps. Scale bar, 5 µm. (C) Representative images of the training and validation dataset to identify SEPT7-positive vs SEPT7-negative bacteria, after processing. Scale bar, 5 µm.
Figure 5 with 1 supplement
Septin assemblies associated with S. flexneri are heterogeneous.
(A) Selected examples of the training dataset to showcase the diverse morphology of septin assemblies associated with bacteria. Frequent morphologies include (a) ring-like structures around the bacteria, (b) smooth tight recruitment around the entire bacterial surface, (c) loose structures more distant from the bacterial surface, (d) septin recruitment to both bacterial poles, (e) septin recruitment to a single bacterial pole. Scale bar, 1 µm. (B) Intensity profiles of the cytosolic bacterial mCherry and the SEPT7 recruitment, as indicated in the white and orange dashed lines, respectively. (C) Pie chart representing the relative frequency of 855 S. flexneri-SEPT7 associations depicted in A.
Figure 5—figure supplement 1
Probability maps of SEPT7 around S. flexneri.
(A, C) Median of the signal for mCherry and SEPT7 in both datasets (SEPT7-positive and SEPT7-negative), centred at the bacterial midbody or bacterial pole, respectively. Only bacteria with width ≥2.5 µm were selected to ensure bacteria were mostly transversal to the acquisition axis. Scale bar, 1 µm. (B, C) Quantification of SEPT7 signal and S. flexneri mCherry signal in the midbody projection depicted in A and transversal dashed line indicated in B.
Figure 6 with 1 supplement
S. flexneri associated with SEPT7 is metabolically active.
(A) Representative microscopy image of HeLa cells infected with S. flexneri expressing mCherry at 3 hpi after 1 hr incubation with Click-iT EdU. Scale bar, 10 µm. (B) Enlarged view of insets in A, highlighting a S. flexneri associated or not to SEPT7 structures. Scale bar, 1 µm. (C) Frequency of the number of SEPT7-associated bacteria identified using deep learning from experiments in A, from n=1024 from 3 independent replicates. Graph represents mean ± SD (9.23% ± 2.62%). (D) Normalised ratio between EdU signal and mCherry in individual bacteria. Graph represents the median (negatives: 1, septin associated: 1.059), and interquartile range (negatives: 0.8185–1.207, septin associated: 0.8573–1.487). Mann-Whitney U test. (E) Representative microscopy image of HeLa cells infected with S. flexneri expressing mCherry at 3 hpi after 1 hr incubation with Click-iT AHA. Scale bar, 10 µm. (F) Enlarged view of insets in E, highlighting a S. flexneri associated or not to SEPT7 assemblies. Scale bar, 1 µm. (G) Frequency of the number of SEPT7-associated bacteria identified using deep learning from experiments in E, from n=1872 bacteria from 3 independent replicates. Graph represents mean ± SD (7.01% ± 2.41%). (D) Normalised AHA signal in individual bacteria in A, B. Graph represents the median (negatives: 1, SEPT7 associated: 1.055) and interquartile range (negatives: 0.6986–1.345, SEPT7 associated: 0.7837–1.497). Mann-Whitney U test.
Figure 6—figure supplement 1
Click-iT EdU and Click-iT AHA specifically label de novo DNA and protein translation in S. flexneri.
(A) Representative Airyscan image of mCherry S. flexneri after Click-iT EdU reaction in the presence or absence of nalidixic acid to block bacterial DNA replication. Scale bar, 1 µm. (B) Quantification of Click-iT EdU signal of bacteria in C measured by FACS. Graph represents normalised mean ± SD. One-way ANOVA and Tukey’s multiple comparisons test. (C) Representative Airyscan confocal images of HeLa cells infected with mCherry expressing S. flexneri after Click-iT EdU reaction in the presence or absence of nalidixic acid to inhibit bacterial DNA nuclear replication. Scale bar, 10 µm. (D) Quantification of de novo DNA synthesis in single bacteria in E as the ratio of nuclear Click-iT signal to Hoechst. Graph represents normalised median (untreated: 1, nalidixic acid: 0.0646) and interquartile range (untreated: 0.3589–1.924, nalidixic acid: 0.0350–0.3080) of 9310 untreated and 4368 nalidixic acid treated bacteria from 3 independent experiments. Mann-Whitney U test. (E) Representative Airyscan confocal images of HeLa cells infected with mCherry expressing S. flexneri after Click-IT AHA reaction in the presence or absence of rifampicin to block prokaryotic translation. Scale bar, 10 µm; insets, 1 µm. (F) Quantification of cytoplasmic Click-iT AHA signal of individual S. flexneri cells in C. Graph represents normalised median (untreated: 1, rifampicin: 0.8263) and interquartile range (untreated: 0.7520–1.350, rifampicin: 0.6143–1.100) of n=2309 untreated and n=345 rifampicin treated bacteria from 3 independent experiments.
Figure 7 with 1 supplement
S. flexneri associated with SEPT7 has an active type III secretion system (T3SS).
(A) Representative microscopy image of HeLa cells infected with S. flexneri expressing the transcription-based secretion activity reporter (TSAR) reporter at 3 hpi. Scale bar, 10 µm. Arroheads highlight TSAR+ bacteria. Selected infected cells’ contours are delimited with dashed lines. (B) Distribution of TSAR signal intensity in individual bacteria. Graph represents the normalised median of the ratio between TSAR and mCherry (1), and interquartile range (0.7827–1.369). Values from n=3654 bacterial cells from 3 independent experiments. An arbitrary threshold of 1.5 is selected to define TSAR+ S. flexneri. (C) Intensity of TSAR signal in individual bacteria depending on infection load of host cells. Graph represents the normalised median and interquartile range. Kruskal-Wallis test and Dunn’s multiple comparisons test. (D) Representative microscopy image of HeLa cells infected with S. flexneri expressing the TSAR reporter at 3 hpi. Scale bar, 10 µm. (E) Enlarged view of insets in D, highlighting a S. flexneri associated or not to SEPT7 assemblies. Scale bar, 1 µm. (F) Frequency of the number of SEPT7-associated bacteria identified using deep learning from experiments in F, from n=5030 bacteria from 4 independent replicates. Graph represents mean ± SD (9.044% ± 5.582%). (G) Percentage of TSAR+ S. flexneri depending on their association to SEPT7. Graph represents mean ± SD (negatives: 2.214±1.603, septin associated: 17.74±5.138). Student’s t-test. (H) Normalised ratio between TSAR signal and mCherry in individual bacteria. Graph represents the median (negatives: 1, SEPT7 associated: 1.122) and interquartile range (negatives: 0.7674–1.286, SEPT7 associated: 0.9047–1.511). Mann-Whitney U test.
Figure 7—figure supplement 1
Transcription-based secretion activity reporter (TSAR) labels type III secretion system (T3SS) activity specifically.
(A) Representative Airyscan confocal images of S. flexneri harbouring the TSAR plasmid grown in broth in the presence or absence of congo red to induce the expression of the T3SS. Scale bar, 1 µm. (B) Quantification of the TSAR-dependent GFP signal of bacteria in A measured by FACS. Graph represents the normalised mean ± SD (untreated: 1±0, congo red: 155.4±125.2) from 4 independent experiments. (C) Representative examples of TSAR-positive S. flexneri associated with SEPT7. C stands for ‘cage’, P stands for ‘protrusion’. Scale bar, 5 µm.
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (Shigella flexneri) | Shigella flexneri srv. 5 a str. M90T | Mostowy et al., 2010 | ||
| strain, strain background (Shigella flexneri) | Shigella flexneri srv. 5 a str. M90T mCherry | Mostowy et al., 2013 | Constitutively producing mCherry | |
| Strain, strain background (Shigella flexneri) | Shigella flexneri srv. 5 a str. M90T afaI | Mostowy et al., 2010 | Constitutively producing the adhesin AfaE | |
| Strain, strain background (Shigella flexneri=) | Shigella flexneri srv. 5 a str. M90T GFP | Mostowy et al., 2010 | Constitutively producing GFP | |
| Strain, strain background (Shigella flexneri) | Shigella flexneri srv. 5 a str. M90T GFP afaI | This study | Constitutively producing the adhesin AfaE and GFP | |
| Cell line (Homo sapiens) | HeLa | ATCC | CCL2 | |
| Antibody | Anti-human SEPT7 (rabbit polyclonal) | IBL | Cat#: 18991 RRID:AB_10705434 | IF (1:100) |
| Recombinant DNA reagent | pTSAR1ud2.4s (plasmid) | Campbell-Valois et al., 2014 | T3SS reporter | |
| Recombinant DNA reagent | pAC-Plac (plasmid) | Lobato-Márquez et al., 2015 | pACYC184 derived plasmid. | |
| Recombinant DNA reagent | pAC-AfaI-AmpR (plasmid) | This study | For constitutive expression of the adhesin AfaE, ori p15A, AmpR | |
| Recombinant DNA reagent | pAC-AfaI-CmR (plasmid) | This study | For constitutive expression of the adhesin AfaE, ori p15A, CmR | |
| Sequence-based reagent | afaI-operon-fw | This study | PCR-primer | tgcgttgcgcgaaga tcctttgatcttttctacgggg |
| Sequence-based reagent | afaI-operon-rv | This study | PCR-primer | ggaagctaaac gagcccgatcttcccca |
| Sequence-based reagent | p15A-ori-fw | This study | PCR-primer | atcgggctcgttt agcttccttagctcc |
| Sequence-based reagent | p15A-ori-rv | This study | PCR-primer | aaggatcttcgcg caacgcaattaatgtaag |
| Sequence-based reagent | afaI-p15A-fw | This study | PCR-primer | cggggcgtaactg tcagaccaagtttac |
| Sequence-based reagent | afaI-p15A-rv | This study | PCR-primer | ttttctccatactctt cctttttcaatattattg |
| Sequence-based reagent | CmR-fw | This study | PCR-primer | aaggaagagtatggag aaaaaaatcactggata taccaccgttgatatatccc |
| Sequence-based reagent | CmR-rv | This study | PCR-primer | ggtctgacagttac gccccgccctgcca |
| Commercial assay or kit | Click-iT EdU Cell Proliferation Kit for Imaging | ThermoFisher Scientific | Cat#: C10337 | |
| Commercial assay or kit | Click-iT AHA Protein Synthesis HCS Assay | ThermoFisher Scientific | Cat#: C10289 | |
| Chemical compound, drug | Congo red | Sigma-Aldrich | Cat#:C6767 | 0.01% (w/v) |
| Chemical compound, drug | Aphidicolin | Sigma-Aldrich | Cat#: A0781 | 50 μg/mL |
| Chemical compound, drug | Cycloheximide | Cell Signalling | Cat#: 2112 | 50 μg/mL |
| Chemical compound, drug | Nalidixic acid | Sigma-Aldrich | Cat#: N8878 | 2 μg/mL |
| Chemical compound, drug | Rifampicin | Sigma-Aldrich | Cat#: R3501 | 100 μg/ml |
| Software, algorithm | CellProfiler | CellProfiler | v.4.0.7 | |
| Other | Alexa-488-conjugated phalloidin | ThermoFisher Scientific | Cat#: 10729174 | IF (1:500) |
| Other | Hoescht | ThermoFisher Scientific | Cat#: H3570 | IF (1:500) |
Table 1
Bacterial strains and plasmids used in this study.
| Bacterial strain | Genotype | Reference |
|---|---|---|
| Shigella flexneri srv. 5a str. M90T | SmR | Mostowy et al., 2010 |
| S. flexneri mCherry | Constitutively producing mCherry, CarbR, SmR | Mostowy et al., 2013 |
| S. flexneri afaI | Constitutively producing the adhesin AfaE, AmpR, SmR | Mostowy et al., 2010 |
| S. flexneri GFP | Constitutively producing GFP, CarbR | Mostowy et al., 2010 |
| S. flexneri GFP afaI | Constitutively producing the adhesin AfaE and GFP, AmpR CmR, SmR | This study |
| Plasmid | Characteristics | Reference |
| pTSAR1ud2.4s | T3SS reporter | Campbell-Valois et al., 2014 |
| pAC-Plac | pACYC184-derived plasmid | Lobato-Márquez et al., 2015 |
| pAC-AfaI-AmpR | For constitutive expression of the adhesin AfaE, ori p15A, AmpR | This study |
| pAC-AfaI-CmR | For constitutive expression of the adhesin AfaE, ori p15A, CmR | This study |
| Primer | Sequence (5’–3’) | Reference |
| afaI-operon-fw | tgcgttgcgcgaagatcctttgatcttttctacgggg | This study |
| afaI-operon-rv | ggaagctaaacgagcccgatcttcccca | This study |
| p15A-ori-fw | atcgggctcgtttagcttccttagctcc | This study |
| p15A-ori-rv | aaggatcttcgcgcaacgcaattaatgtaag | This study |
| afaI-p15A-fw | cggggcgtaactgtcagaccaagtttac | This study |
| afaI-p15A-rv | ttttctccatactcttcctttttcaatattattg | This study |
| CmR-fw | aaggaagagtatggagaaaaaaatcactggatataccaccgttgatatatccc | This study |
| CmR-rv | ggtctgacagttacgccccgccctgcca | This study |
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High-content high-resolution microscopy and deep learning-assisted analysis reveals host and bacterial heterogeneity during Shigella infection
eLife 13:RP97495.
https://doi.org/10.7554/eLife.97495.3
