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Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.

Lin JR, Fallahi-Sichani M, Sorger PK - Nat Commun (2015)

Bottom Line: Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers.Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited.Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.

View Article: PubMed Central - PubMed

Affiliation: HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA.

ABSTRACT
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.

No MeSH data available.


Related in: MedlinePlus

Compatibility of CycIF with live-cell imaging, analysis of subcellular localization of proteins and morphometric features.(a) Applying CycIF at the end of FP-based live-cell imaging. Nuclear translocation of FoxO3a in MCF10A cells (stably expressing YFP-FoxO3a and mCherry-NLS) was monitored for 24 h (left panel). Red arrows indicate FoxO3a nuclear to cytoplasmic (N/C) ratios at two snapshots. Cells were then fixed and subjected to four-cycle CycIF using fluorophore-conjugated antibodies p-RbS807/S811 (cycle 1), p21 and PCNA (cycle 2), EGFR and β-tubulin (cycle 3) and Ki-67 and p-S6S235/6 (cycle 4) (right panel). (b) Morphometric analysis of CycIF-generated images of single cells. Three rounds of CycIF staining were performed as indicated in Supplementary Fig. S8. The raw intensity-based images of EGFR, VEGFR2, Tubulin, Vimentin and PCNA were then binarized and passed through different filters (sharpen, skeletonized and maximum, and so on) for extracting texture features (length, branches, enrichment and clusters). Two representative images/masks from Tubulin/microtubule and EGFR are shown here. Detailed methods and other images/masks could be found in Supplementary Information. (c) Single-cell clustering based on morphometric features. Sixteen different features were extracted from five different IF signals. The numerical values from features were used in hierarchical clustering of single cells (left panel). Two sub-clusters were displayed with distinct features generated from PCNA and VEGFR2 (right panel). The hierarchical clustering was performed in Matlab using Euclidean distance metrics and average linkage.
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f2: Compatibility of CycIF with live-cell imaging, analysis of subcellular localization of proteins and morphometric features.(a) Applying CycIF at the end of FP-based live-cell imaging. Nuclear translocation of FoxO3a in MCF10A cells (stably expressing YFP-FoxO3a and mCherry-NLS) was monitored for 24 h (left panel). Red arrows indicate FoxO3a nuclear to cytoplasmic (N/C) ratios at two snapshots. Cells were then fixed and subjected to four-cycle CycIF using fluorophore-conjugated antibodies p-RbS807/S811 (cycle 1), p21 and PCNA (cycle 2), EGFR and β-tubulin (cycle 3) and Ki-67 and p-S6S235/6 (cycle 4) (right panel). (b) Morphometric analysis of CycIF-generated images of single cells. Three rounds of CycIF staining were performed as indicated in Supplementary Fig. S8. The raw intensity-based images of EGFR, VEGFR2, Tubulin, Vimentin and PCNA were then binarized and passed through different filters (sharpen, skeletonized and maximum, and so on) for extracting texture features (length, branches, enrichment and clusters). Two representative images/masks from Tubulin/microtubule and EGFR are shown here. Detailed methods and other images/masks could be found in Supplementary Information. (c) Single-cell clustering based on morphometric features. Sixteen different features were extracted from five different IF signals. The numerical values from features were used in hierarchical clustering of single cells (left panel). Two sub-clusters were displayed with distinct features generated from PCNA and VEGFR2 (right panel). The hierarchical clustering was performed in Matlab using Euclidean distance metrics and average linkage.

Mentions: CycIF can be effectively combined with live-cell imaging. In the four-cycle, 9-channel experiment shown in Fig. 2a, we monitored nuclear translocation of FoxO3a in MCF10A cells stably expressing YFP-FoxO3a and mCherry-NLS for 24 h followed by fixation and four cycles of fluorophore inactivation and antibody staining. We used conjugated antibodies to measure cell cycle state (p-RbS807/S811, p21, Ki-67 and PCNA; see Supplementary Tables 1 and 2 for the list of validated antibodies and abbreviations), signal transduction (EGFR and p-S6S235/6) and cell morphology (microtubules). In other experiments we also used dyes such as MitoTracker Red to label mitochondria and other organelles (Supplementary Fig. 7). The choice of which antibodies to combine at each step is made empirically, with the goal of assigning Alexa 647 to the weakest signals since the near-infrared has less autofluorescence. Relative to flow cytometry (or CyTOF), imaging adds information on cell morphology and protein translocation, which is particularly useful for studying the cytoskeleton, membrane receptor clustering and nuclear foci. In the images shown in Fig. 2b and Supplementary Fig. 8, we performed three rounds of CycIF and morphometric features then extracted from five channels using Bayesian classifiers and similar methods26. The dimensionality of such data is substantially higher than the number of unique antibodies or dyes and has the advantage, relative to flow cytometry, that features such as nuclear foci or receptor clusters can be scored and correlated (Fig. 2c). For example, VEGFR2 clustering appears to be negatively correlated with the number of PCNA foci, which is a marker of S-phase (Fig. 2c, left panels).


Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.

Lin JR, Fallahi-Sichani M, Sorger PK - Nat Commun (2015)

Compatibility of CycIF with live-cell imaging, analysis of subcellular localization of proteins and morphometric features.(a) Applying CycIF at the end of FP-based live-cell imaging. Nuclear translocation of FoxO3a in MCF10A cells (stably expressing YFP-FoxO3a and mCherry-NLS) was monitored for 24 h (left panel). Red arrows indicate FoxO3a nuclear to cytoplasmic (N/C) ratios at two snapshots. Cells were then fixed and subjected to four-cycle CycIF using fluorophore-conjugated antibodies p-RbS807/S811 (cycle 1), p21 and PCNA (cycle 2), EGFR and β-tubulin (cycle 3) and Ki-67 and p-S6S235/6 (cycle 4) (right panel). (b) Morphometric analysis of CycIF-generated images of single cells. Three rounds of CycIF staining were performed as indicated in Supplementary Fig. S8. The raw intensity-based images of EGFR, VEGFR2, Tubulin, Vimentin and PCNA were then binarized and passed through different filters (sharpen, skeletonized and maximum, and so on) for extracting texture features (length, branches, enrichment and clusters). Two representative images/masks from Tubulin/microtubule and EGFR are shown here. Detailed methods and other images/masks could be found in Supplementary Information. (c) Single-cell clustering based on morphometric features. Sixteen different features were extracted from five different IF signals. The numerical values from features were used in hierarchical clustering of single cells (left panel). Two sub-clusters were displayed with distinct features generated from PCNA and VEGFR2 (right panel). The hierarchical clustering was performed in Matlab using Euclidean distance metrics and average linkage.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4587398&req=5

f2: Compatibility of CycIF with live-cell imaging, analysis of subcellular localization of proteins and morphometric features.(a) Applying CycIF at the end of FP-based live-cell imaging. Nuclear translocation of FoxO3a in MCF10A cells (stably expressing YFP-FoxO3a and mCherry-NLS) was monitored for 24 h (left panel). Red arrows indicate FoxO3a nuclear to cytoplasmic (N/C) ratios at two snapshots. Cells were then fixed and subjected to four-cycle CycIF using fluorophore-conjugated antibodies p-RbS807/S811 (cycle 1), p21 and PCNA (cycle 2), EGFR and β-tubulin (cycle 3) and Ki-67 and p-S6S235/6 (cycle 4) (right panel). (b) Morphometric analysis of CycIF-generated images of single cells. Three rounds of CycIF staining were performed as indicated in Supplementary Fig. S8. The raw intensity-based images of EGFR, VEGFR2, Tubulin, Vimentin and PCNA were then binarized and passed through different filters (sharpen, skeletonized and maximum, and so on) for extracting texture features (length, branches, enrichment and clusters). Two representative images/masks from Tubulin/microtubule and EGFR are shown here. Detailed methods and other images/masks could be found in Supplementary Information. (c) Single-cell clustering based on morphometric features. Sixteen different features were extracted from five different IF signals. The numerical values from features were used in hierarchical clustering of single cells (left panel). Two sub-clusters were displayed with distinct features generated from PCNA and VEGFR2 (right panel). The hierarchical clustering was performed in Matlab using Euclidean distance metrics and average linkage.
Mentions: CycIF can be effectively combined with live-cell imaging. In the four-cycle, 9-channel experiment shown in Fig. 2a, we monitored nuclear translocation of FoxO3a in MCF10A cells stably expressing YFP-FoxO3a and mCherry-NLS for 24 h followed by fixation and four cycles of fluorophore inactivation and antibody staining. We used conjugated antibodies to measure cell cycle state (p-RbS807/S811, p21, Ki-67 and PCNA; see Supplementary Tables 1 and 2 for the list of validated antibodies and abbreviations), signal transduction (EGFR and p-S6S235/6) and cell morphology (microtubules). In other experiments we also used dyes such as MitoTracker Red to label mitochondria and other organelles (Supplementary Fig. 7). The choice of which antibodies to combine at each step is made empirically, with the goal of assigning Alexa 647 to the weakest signals since the near-infrared has less autofluorescence. Relative to flow cytometry (or CyTOF), imaging adds information on cell morphology and protein translocation, which is particularly useful for studying the cytoskeleton, membrane receptor clustering and nuclear foci. In the images shown in Fig. 2b and Supplementary Fig. 8, we performed three rounds of CycIF and morphometric features then extracted from five channels using Bayesian classifiers and similar methods26. The dimensionality of such data is substantially higher than the number of unique antibodies or dyes and has the advantage, relative to flow cytometry, that features such as nuclear foci or receptor clusters can be scored and correlated (Fig. 2c). For example, VEGFR2 clustering appears to be negatively correlated with the number of PCNA foci, which is a marker of S-phase (Fig. 2c, left panels).

Bottom Line: Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers.Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited.Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.

View Article: PubMed Central - PubMed

Affiliation: HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA.

ABSTRACT
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.

No MeSH data available.


Related in: MedlinePlus