Comparative Analysis of SPLICS and MCS-DETECT for Detecting Mitochondria-ER Contact Sites (MERCs)
Jieyi Zheng1, Ben Cardoen2, Milene Ortiz-Silva1
1Department of Cellular & Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Abstract:
Detection of mitochondria-ER contacts (MERCs) from diffraction limited confocal images commonly uses fluorescence colocalization analysis of mitochondria and endoplasmic reticulum (ER) as well as split fluorescent probes, such as the split-GFP-based contact site sensor (SPLICS). However, inter-organelle distances (∼10-60 nm) for MERCs are lower than the 200-250 nm diffraction limited resolution obtained by standard confocal microscopy. Super-resolution microscopy of 3D volume analysis provides a two-fold resolution improvement (∼120 nm XY; 250 nm Z), which remains unable to resolve MERCs. MCS-DETECT, a membrane contact site (MCS) detection algorithm faithfully detects elongated ribosome-studded riboMERCs when applied to 3D STED super-resolution image volumes. Here, we expressed the SPLICSL reporter in HeLa cells co-transfected with the ER reporter RFP-KDEL and label fixed cells with antibodies to RFP and the mitochondrial protein TOM20. MCS-DETECT analysis of 3D STED volumes was compared to contacts determined by co-occurrence colocalization analysis of mitochondria and ER or the SPLICSL probe. Percent mitochondria coverage by MCS-DETECT derived contacts was significantly smaller than those obtained for colocalization analysis or SPLICSL, and more closely matched contact site metrics obtained by 3D electron microscopy. Further, STED analysis localized a subset of the SPLICSL label to mitochondria with some SPLICSL puncta observed to be completely enveloped by mitochondria in 3D views. These data suggest that MCS-DETECT reports on a limited set of MERCs that more closely corresponds to those observed by EM.
Insights
MCS-DETECT accurately identifies mitochondria-ER contacts (MERCs) using super-resolution microscopy, providing results closer to electron microscopy than traditional methods. This algorithm offers improved detection of these crucial cellular junctions.
Area of Science:
- Cell Biology
- Microscopy
- Organelle Interactions
Background:
- Mitochondria-ER contacts (MERCs) are vital for cellular function but challenging to detect due to their small size.
- Conventional confocal microscopy and split fluorescent probes (SPLICS) have limitations in resolving MERCs at nanometer scales.
- Super-resolution microscopy improves resolution but still struggles to precisely delineate MERCs.
Purpose of the Study:
- To evaluate the efficacy of the MCS-DETECT algorithm for identifying MERCs using 3D STED super-resolution microscopy.
- To compare MCS-DETECT findings with traditional colocalization analysis and SPLICS probe data.
- To validate MCS-DETECT's performance against 3D electron microscopy (EM) standards.
Main Methods:
- Utilized 3D STED super-resolution microscopy in HeLa cells expressing SPLICS and ER/mitochondria reporters.
- Applied the MCS-DETECT algorithm to analyze 3D STED image volumes.
- Compared MCS-DETECT results with co-occurrence colocalization analysis and SPLICS probe localization.
- Validated findings against 3D electron microscopy data for contact site metrics.
Main Results:
- MCS-DETECT successfully detected elongated riboMERCs in 3D STED volumes.
- The percentage of mitochondria covered by MCS-DETECT identified contacts was significantly smaller than colocalization or SPLICS methods.
- MCS-DETECT contact site metrics closely aligned with those obtained from 3D electron microscopy.
- STED analysis revealed SPLICS localization on mitochondria, with some puncta fully enveloped by mitochondria.
Conclusions:
- MCS-DETECT provides a more accurate and refined detection of MERCs compared to conventional methods.
- The algorithm's findings correlate better with ultrastructural data from electron microscopy.
- MCS-DETECT is a valuable tool for studying specific subsets of MERCs, particularly those with distinct structural features.
