scholarly journals Lights up on organelles: Optogenetic tools to control subcellular structure and organization

Author(s):  
Therese C. Kichuk ◽  
César Carrasco-López ◽  
José L. Avalos
1997 ◽  
Vol 139 (3) ◽  
pp. 817-829 ◽  
Author(s):  
Michaela Wilsch-Bräuninger ◽  
Heinz Schwarz ◽  
Christiane Nüsslein-Volhard

Localization of maternally provided RNAs during oogenesis is required for formation of the antero–posterior axis of the Drosophila embryo. Here we describe a subcellular structure in nurse cells and oocytes which may function as an intracellular compartment for assembly and transport of maternal products involved in RNA localization. This structure, which we have termed “sponge body,” consists of ER-like cisternae, embedded in an amorphous electron-dense mass. It lacks a surrounding membrane and is frequently associated with mitochondria. The sponge bodies are not identical to the Golgi complexes. We suggest that the sponge bodies are homologous to the mitochondrial cloud in Xenopus oocytes, a granulo-fibrillar structure that contains RNAs involved in patterning of the embryo. Exuperantia protein, the earliest factor known to be required for the localization of bicoid mRNA to the anterior pole of the Drosophila oocyte, is highly enriched in the sponge bodies but not an essential structural component of these. RNA staining indicates that sponge bodies contain RNA. However, neither the intensity of this staining nor the accumulation of Exuperantia in the sponge bodies is dependent on the amount of bicoid mRNA present in the ovaries. Sponge bodies surround nuage, a possible polar granule precursor. Microtubules and microfilaments are not present in sponge bodies, although transport of the sponge bodies through the cells is implied by their presence in cytoplasmic bridges. We propose that the sponge bodies are structures that, by assembly and transport of included molecules or associated structures, are involved in localization of mRNAs in Drosophila oocytes.


2021 ◽  
Author(s):  
Rory Donovan-Maiye ◽  
Jackson Brown ◽  
Caleb Chan ◽  
Liya Ding ◽  
Calysta Yan ◽  
...  

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.


2018 ◽  
Author(s):  
Chawin Ounkomol ◽  
Sharmishtaa Seshamani ◽  
Mary M. Maleckar ◽  
Forrest Collman ◽  
Gregory R. Johnson

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.


1978 ◽  
Vol 174 (3) ◽  
pp. 939-949 ◽  
Author(s):  
M J S De Wolf ◽  
A R Lagrou ◽  
H J J Hilderson

1. After differential pelleting of bovine thyroid tissue the highest relative specific activities for plasma membrane markers are found in the L fraction whereas those for peroxidase activities (p-phenylenediamine, guaiacol and 3,3′-diaminobenizidine tetrachloride peroxidases) are found in the M fraction. 2. When M + L fractions were subjected to buoyant-density equilibration in a HS zonal rotor all peroxidases show different profiles. The guaiacol peroxidase activity always follows the distribution of glucose 6-phosphatase. 3. When a Sb fraction is subjected to Sepharose 2B chromatography three major peaks are obtained. The first, eluted at the void volume, consists of membranous material and contains most of the guaiacol peroxidase activity. Most of the protein (probably thyroglobulin) is eluted with the second peak. Solubilized enzymes are recovered in the third peak. 4. p-Phenylenediamine peroxidase activity penetrates into the gel on polyacrylamidegel electrophoresis, whereas guaiacol peroxidase activity remains at the sample zone. 5. DEAE-Sephadex A-50 chromatography resolves the peroxidase activities into two peaks, displaying different relative amounts of the different enzymic activities in each peak. 6. The peroxidase activities may be due to the presence of different proteins. A localization of guaiacol peroxidase in rough-endoplasmic-reticulum membranes (or in membranes related to them) seems very likely.


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