Quantitatively Imaging Chromosomes by Correlated Cryo-Fluorescence and Soft X-Ray Tomographies

Smith, Elizabeth A.; McDermott, Gerry; Do, Myan; Leung, Karen; Panning, Barbara; Gros, Mark A. Le; Larabell, Carolyn A.

doi:10.1016/j.bpj.2014.09.011

Cited by 75 publications

(74 citation statements)

References 44 publications

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“…We previously demonstrated the potential of cryo-SXT to detect pericentromeric heterochromatin foci in the nuclei (45). Pericentromeric heterochromatin has higher linear absorption coefficients (LAC) (between 0.34-0.36 μm −1 ) than the rest of the heterochromatin, which allows their distinction from other heterochromatic regions and euchromatin (46). Our experiments on lymphoblastoid cells revealed clusters of pericentromeric heterochromatin in the interior regions of the nucleus, consistent with our findings (Fig.…”

Section: Assessment Of Our Structure Population With a Diverse Collecsupporting

confidence: 91%

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Tjong

Kalhor

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

199

232

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Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization. (14), and single-cell (15) and in situ Hi-C (16)], close chromatin contacts can now be identified at increasing resolution, providing new insight into genome organization. These methods measure the relative frequencies of chromosome interactions averaged over a large population of cells. However, individual 3D genome structures can vary dramatically from cell to cell even within an isogenic sample, especially with respect to long-range interactions (15,17,18). This structural variability poses a great challenge to the interpretation of ensemble-averaged Hi-C data (14,(19)(20)(21)(22)(23) and prevents the direct detection of cooperative interactions co-occurring in the same cell. This problem is particularly evident for long-range (cis) and interchromosomal (trans) interactions, which are generally observed at relatively low frequencies and are therefore present only in a small subset of individual cells at any given time (3,11,15). Despite their low frequencies, long-range and interchromosome interaction patterns are not random noise. In fact, these interactions are more informative than short-range interactions in determining the global genome architectures in cells and are often functionally relevant-interactions between transcriptionally active regions are often interchromosomal in nature (14). Owing to their variable nature, long-range and trans interactions can be part of alternative, structurally different conformations, which makes their interpretation in form of consensus structures impossible. However, inferring which of the long-range interactions co-occur in the same cell from ensemble Hi-C data remains a major challenge.These challenges cannot be easily overcome even by the new single-cell Hi-C techno...

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Section: Assessment Of Our Structure Population With a Diverse Collecsupporting

confidence: 91%

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Tjong

Kalhor

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

199

232

View full text Add to dashboard Cite

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“…Here, we investigate the link between MiD49, MiD51 and the ER in mitochondrial constriction and fission using a unique approach that combines confocal live-cell imaging, correlative cryogenic fluorescence microscopy and soft x-ray tomography (CFM-SXT) (Larabell and Nugent, 2010;McDermott et al, 2012;Parkinson et al, 2013;Smith et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Analysis of ER-mitochondria contacts by correlative fluorescence microscopy and soft X-ray tomography of mammalian cells

Elgass

Smith

LeGros

et al. 2015

Journal of Cell Science

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Mitochondrial fission is important for organelle transport, quality control and apoptosis. Changes to the fission process can result in a wide variety of neurological diseases. In mammals, mitochondrial fission is executed by the GTPase dynamin-related protein 1 (Drp1; encoded by DNM1L), which oligomerizes around mitochondria and constricts the organelle. The mitochondrial outer membrane proteins Mff, MiD49 (encoded by MIEF2) and MiD51 (encoded by MIEF1) are involved in mitochondrial fission by recruiting Drp1 from the cytosol to the organelle surface. In addition, endoplasmic reticulum (ER) tubules have been shown to wrap around and constrict mitochondria before a fission event. Up to now, the presence of MiD49 and MiD51 at ER-mitochondrial division foci has not been established. Here, we combine confocal live-cell imaging with correlative cryogenic fluorescence microscopy and soft x-ray tomography to link MiD49 and MiD51 to the involvement of the ER in mitochondrial fission. We gain further insight into this complex process and characterize the 3D structure of ER-mitochondria contact sites.

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“…LAC values of specific cellular components in a single cell population, and also in different cell types across many eukaryotic species, consistently fall within a certain range. Mammalian nuclear heterochromatin typically ranges from 0.25 to 0.36 μm −1 , and euchromatin, 0.13–0.25 μm −1 [33]. Similarly, the LAC of nuclear chromatin in yeast is 0.26 ± 0.01 μm −1 [57].…”

Section: Interpreting and Analyzing Soft X-ray Reconstructionsmentioning

confidence: 99%

“…Information on the topology and packaging of the genome in the nucleus is invaluable to the generation of mathematical models that describe, for example, the movement of transcription factors and other molecules within the packed nuclear space [32]. Finally, the CFT data is used to unambiguously identify fluorescently labeled chromosome territories, creating a perfect structural complement to high-throughput molecular biology techniques, such as chromosome conformation capture (3C) [33]. …”

Section: Introductionmentioning

confidence: 99%

Imaging and characterizing cells using tomography

Isaacson

McDermott

et al. 2015

Archives of Biochemistry and Biophysics

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We can learn much about cell function by imaging and quantifying sub-cellular structures, especially if this is done non-destructively without altering said structures. Soft x-ray tomography (SXT) is a high-resolution imaging technique for visualizing cells and their interior structure in 3D. A tomogram of the cell, reconstructed from a series of 2D projection images, can be easily segmented and analyzed. SXT has a very high specimen throughput compared to other high-resolution structure imaging modalities; for example, tomographic data for reconstructing an entire eukaryotic cell is acquired in a matter of minutes. SXT visualizes cells without the need for chemical fixation, dehydration, or staining of the specimen. As a result, the SXT reconstructions are close representations of cells in their native state. SXT is applicable to most cell types. The deep penetration of soft x-rays allows cells, even mammalian cells, to be imaged without being sectioned. Image contrast in SXT is generated by the differential attenuation soft x-ray illumination as it passes through the specimen. Accordingly, each voxel in the tomographic reconstruction has a measured linear absorption coefficient (LAC) value. LAC values are quantitative and give rise to each sub-cellular component having a characteristic LAC profile, allowing organelles to be identified and segmented from the milieu of other cell contents. In this chapter, we describe the fundamentals of SXT imaging and how this technique can answer real world questions in the study of the nucleus. We also describe the development of correlative methods for the localization of specific molecules in a SXT reconstruction. The combination of fluorescence and SXT data acquired from the same specimen produces composite 3D images, rich with detailed information on the inner workings of cells.

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Quantitatively Imaging Chromosomes by Correlated Cryo-Fluorescence and Soft X-Ray Tomographies

Cited by 75 publications

References 44 publications

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Analysis of ER-mitochondria contacts by correlative fluorescence microscopy and soft X-ray tomography of mammalian cells

Imaging and characterizing cells using tomography

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