Iterative immunostaining combined with expansion microscopy and image processing reveals nanoscopic network organization of nuclear lamina
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Abstract
Investigation of nuclear lamina architecture relies on super-resolved microscopy. However, epitope accessibility, labeling density, and detection precision of individual molecules pose challenges within the molecularly crowded nucleus. We developed iterative indirect immunofluorescence (IT–IF) staining approach combined with expansion microscopy (ExM) and structured illumination microscopy to improve super-resolution microscopy of subnuclear nanostructures like lamins. We prove that ExM is applicable in analyzing highly compacted nuclear multiprotein complexes such as viral capsids and provide technical improvements to ExM method including 3D-printed gel casting equipment. We show that in comparison to conventional immunostaining, IT-IF results in a higher signal-to-background –ratio and a mean fluorescence intensity by improving the labeling density. Moreover, we present a signal processing pipeline for noise estimation, denoising, and deblurring to aid in quantitative image analyses and provide this platform for the microscopy imaging community. Finally, we show the potential of signal-resolved IT–IF in quantitative super-resolution ExM imaging of nuclear lamina and reveal nanoscopic details of the lamin network organization - a prerequisite for studying intranuclear structural co-regulation of cell function and fate. (Words: 175)
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It looks like each iteration involves permeabilization. How necessary is this step in each iteration rather than simply rounds of primary and secondary antibodies with washes in between?
Also we’ve found detection of actin with fluorescent phalloidin to require improved signal and reduced background, particularly in photosynthetic cells with a great deal of chlorophyll autofluorescence. This requires 1) low concentrations, 2) reduced incubation time, and 3) brighter fluorophore such as Atto compared to Alexa dyes. For your antibodies, you mention low concentrations also result in low signal, so this is suboptimal so I’m still wondering if we could improve phalloidin staining for difficult samples with iteration. I know you don’t find similar improvements in phalloidin staining with iteration but I’m wondering if your phalloidin staining …
It looks like each iteration involves permeabilization. How necessary is this step in each iteration rather than simply rounds of primary and secondary antibodies with washes in between?
Also we’ve found detection of actin with fluorescent phalloidin to require improved signal and reduced background, particularly in photosynthetic cells with a great deal of chlorophyll autofluorescence. This requires 1) low concentrations, 2) reduced incubation time, and 3) brighter fluorophore such as Atto compared to Alexa dyes. For your antibodies, you mention low concentrations also result in low signal, so this is suboptimal so I’m still wondering if we could improve phalloidin staining for difficult samples with iteration. I know you don’t find similar improvements in phalloidin staining with iteration but I’m wondering if your phalloidin staining protocol had been optimized as much as you’ve optimized the antibody concentrations for your 1X designations to minimize signal to noise before performing phalloidin iterations to increase signal. Further, it could be that the absolute target abundance matters for the changes in signal to noise ratio with iteration. So by comparing iteration for low abundance antibody targets with iteration for high abundance non-antibody labeling, you may not see comparable improvements for a non-antibody label. If you were to use this protocol on cells where the f-actin signal is harder to detect than in these cells, might you see improvements with iterative labeling? Or alternatively if you compared antibody iteration with a non-antibody label against a target of lower abundance in the same cells, perhaps the signal to noise difference would change more with iteration? It would be of general interest if iterative staining could also improve signal to noise for low signal or low abundance non-antibody labels.
Thanks for publishing this interesting study!
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It looks like each iteration involves permeabilization. How necessary is this step in each iteration rather than simply rounds of primary and secondary antibodies with washes in between?
Also we’ve found detection of actin with fluorescent phalloidin to require improved signal and reduced background, particularly in photosynthetic cells with a great deal of chlorophyll autofluorescence. This requires 1) low concentrations, 2) reduced incubation time, and 3) brighter fluorophore such as Atto compared to Alexa dyes. For your antibodies, you mention low concentrations also result in low signal, so this is suboptimal so I’m still wondering if we could improve phalloidin staining for difficult samples with iteration. I know you don’t find similar improvements in phalloidin staining with iteration but I’m wondering if your phalloidin staining …
It looks like each iteration involves permeabilization. How necessary is this step in each iteration rather than simply rounds of primary and secondary antibodies with washes in between?
Also we’ve found detection of actin with fluorescent phalloidin to require improved signal and reduced background, particularly in photosynthetic cells with a great deal of chlorophyll autofluorescence. This requires 1) low concentrations, 2) reduced incubation time, and 3) brighter fluorophore such as Atto compared to Alexa dyes. For your antibodies, you mention low concentrations also result in low signal, so this is suboptimal so I’m still wondering if we could improve phalloidin staining for difficult samples with iteration. I know you don’t find similar improvements in phalloidin staining with iteration but I’m wondering if your phalloidin staining protocol had been optimized as much as you’ve optimized the antibody concentrations for your 1X designations to minimize signal to noise before performing phalloidin iterations to increase signal. Further, it could be that the absolute target abundance matters for the changes in signal to noise ratio with iteration. So by comparing iteration for low abundance antibody targets with iteration for high abundance non-antibody labeling, you may not see comparable improvements for a non-antibody label. If you were to use this protocol on cells where the f-actin signal is harder to detect than in these cells, might you see improvements with iterative labeling? Or alternatively if you compared antibody iteration with a non-antibody label against a target of lower abundance in the same cells, perhaps the signal to noise difference would change more with iteration? It would be of general interest if iterative staining could also improve signal to noise for low signal or low abundance non-antibody labels.
Thanks for publishing this interesting study!
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