This study was supported in part by a grant-in-aid for scientific research from the Ministry of Education, Culture, Sports, and Technology of Japan (18K04843) to Y. Yawata, the JST ERATO (JPMJER1502) to N. Nomura.

1. Preparation of the sample
<ol>
	<li>Place a 1 mm thick silicone gasket with wells on a glass slide.</li>
	<li>Place a 1 mm thick 0.8% (w/v) agarose slab in the well of the silicone gasket.</li>
	<li>Dilute the cell density of an arbitrary microbial cell culture to an optical density at 600 nm (OD660) = 1.0.</li>
	<li>Place a 5 &#181;L aliquot of cell suspension on the agarose slab.</li>
	<li>Cover gently with a glass coverslip.</li>
</ol>
2. Setup of a microscope
NOTE: The CRIF technique combines confocal reflection microscopy (CRM) and multichannel confocal microspectroscopy. CRM serves as the source of information for cellular morphology and spatial localization, which is independent from cellular innate fluorescence. Multichannel confocal microspectroscopy provides the spectral information of cellular innate fluorescence. In the following protocol, any image acquired with CRM or confocal fluorescence microspectroscopy is referred to as a CRM image or multichannel confocal microspectroscopy image, respectively.
<ol>
	<li>Connect a confocal microscope with descanned spectral channels to a photomultiplier tube (PMT) or GaAsP detector. 
	NOTE: These setups are available from several manufacturers.</li>
	<li>Equip the microscope with a high numerical&#160;aperture&#160;(NA) objective with adequate magnification. 
	NOTE: A 63x objective with NA &#62; 1.4 is recommended for analyzing bacterial cells.</li>
	<li>Equip the microscope with a half-reflection mirror (e.g., NT 80/20) to accommodate CRM, which relies on the cellular scatter of incident light to visualize cell morphology.</li>
	<li>For multichannel confocal microspectroscopy, equip the microscope with dichroic mirrors. For example, use MBS InVis405, MBS458, MBS488, MBS458/514, MBS488/543, or MBS 488/543/633 beam splitters for 405, 458, 488, 514, 543, or 633 nm excitation, respectively.</li>
	<li>Adjust the illumination intensity for each excitation wavelength using a laser power meter. Keep the output under the microscope constant through excitation wavelengths (e.g., use 50 &#181;W with the 63x objective).</li>
</ol>
3. Image acquisition
<ol>
	<li>Set the pinhole size to 1 AU using the microscope software.</li>
	<li>Set the pixel dwell time (i.e., scanning speed) for each excitation wavelength. 
	NOTE: An excessively long pixel dwell time can damage cells. Avoid excessively long pixel dwell time to minimize photodamage to the cells. For bacterial samples, a pixel dwell time &#60;55.6 &#181;s/&#181;m2 (when the irradiance output under the microscope is ~17 &#181;W/cm2) is usually suitable to avoid growth inhibition. This parameter may vary depending on the organisms and experimental setups.</li>
	<li>Set the scanning resolution. For small cells such as bacteria, use a scanning area of 1,024 x 1,024.</li>
	<li>Set the Z-scanning range so that the region of interest is covered. 
	NOTE: For bacterial and yeast cell samples distributed on an agarose slab, a Z-scanning range of ~15 &#181;m is usually sufficient.</li>
	<li>Set the descanned detector to capture the visible wavelength range (e.g., 416&#8722;691 nm). Use a spectral window of 8&#8722;10 nm.</li>
	<li>Acquire multichannel confocal microspectroscopy images in a sequence from longest to shortest excitation wavelengths to create Z-stacks of fluorescence images.</li>
	<li>Acquire CRM images.</li>
	<li>Save the acquired images as 16-bit tiff files into a directory. Name the files using the naming convention XXXcYYzZZ.tif, where XXX is the excitation wavelength, YY is the detector channel number, ZZ is the Z-slice number, and &#8220;c&#8221; and &#8220;z&#8221; are prefixes for the detector number and the Z-slice number, respectively.
	<ol>
		<li>For example, if a multichannel confocal microspectroscopy image is taken with an excitation wavelength of 405 nm, 1st detector channel of the detector array, and is the 5th slice of the Z-stack, name it &#8220;405c01z05.tif&#8221;. For CRM images, use the string &#8220;CRM&#8221; in place of XXX (e.g., &#8220;CRMc01z05.tif&#8221;). 
		NOTE: Decide whether 2D or 3D segmentation is most suitable for the image data. Use a 2D segmentation method in situations where small cells are constrained to a 2D plane (e.g., bacterial population adhering to a glass surface). Use the 3D segmentation method in situations where the cell population is distributed in a three-dimensional space (e.g., biofilms and tissue samples) or the cell sizes are larger than the thickness of the optical slice (e.g., yeast cells, mammalian cells). For 2D segmentation refer to section 4; for 3D segmentation, refer to section 5.</li>
	</ol>
	</li>
</ol>
4. 2D image analysis
<ol>
	<li>Equip a workstation with image analysis software (e.g., MATLAB).</li>
	<li>Perform cell segmentation and reconstruction of single-cell innate fluorescence signatures.
	<ol>
		<li>Open the image analysis software.</li>
		<li>Double-click and open one of the provided scripts &#8220;Script2D.m&#8221;.</li>
		<li>Go to the Editor tab, then click Run. A folder selection window should appear.</li>
		<li>Select the directory created in step 3.8, then click Open to proceed. A dialogue box that prompts the input of the segmentation parameter will automatically appear. 
		NOTE: For test purposes select the provided dataset (&#8220;Sample_2D&#8221;). The sample dataset is provided as a compressed file and should be extracted in advance.</li>
		<li>Input the segmentation parameters: Threshold of Image Binarization (0&#8722;1) for Image Binarization = 0.45, Upper Threshold For A Cell Region (in pixels) = 200, Lower Threshold for a Cell Region (in pixels) = 10, and The Number of Detectors = 32. Click OK to proceed. 
		NOTE: These parameters may require adjustment depending on the image quality.</li>
		<li>A new image window presenting a CRM image should appear. Select an arbitrary background region (i.e., area where cells are absent) to use for background subtraction. Draw a rectangle within the CRM image by mouse dragging. Double-click within the selected region to confirm the selection.</li>
		<li>Find a new directory named Signature in the same directory selected in step 4.2.4. 
		NOTE: The provided code automatically creates this directory. The &#8220;Signature&#8221; directory stores the innate fluorescence signature of each microbial cell within a population as .png files that are serially numbered after a common prefix &#8220;Signature&#8221;.</li>
	</ol>
	</li>
</ol>
5. 3D image analysis
<ol>
	<li>Equip a workstation with the image analysis software (Table of Materials).</li>
	<li>Perform cell segmentation and reconstruction of single-cell innate fluorescence signatures.
	<ol>
		<li>Open the image analysis software.</li>
		<li>Double-click and open the provided script &#8220;Script3D.m&#8221;.</li>
		<li>Go to the Editor tab, then click Run. A folder selection window should appear.</li>
		<li>Select the directory created in step 3.8, then click Open to proceed. A dialogue box that prompts the input of the segmentation parameters will automatically appear. 
		NOTE: For test purposes select the provided dataset (&#8220;Sample_3D&#8221;). The sample dataset is provided as a compressed file and should be extracted in advance.</li>
		<li>Input the segmentation parameters: Threshold of Image Binarization (0&#8211;1) for image binarization = 0.01, Upper Threshold for a Cell Volume (in pixels) = 1,000, Lower Threshold for a Cell Region (in pixels) = 20, X Pixel Size [&#956;m/pixel] = 0.26, Y Pixel Size [&#956;m/pixel] = 0.26, Z pixel Size [&#956;m/pixel] = 0.42, and The Number of Detectors = 32. Click OK to proceed; a dialogue box that prompts the input for the number of excitation wavelengths will appear. 
		NOTE: These parameters may require adjustment depending on the image quality.</li>
		<li>Input the number of wavelengths used for image acquisition (e.g., if 405, 488, 561, 630 are used, enter 4 in the dialogue box). Click OK.</li>
		<li>Enter the excitation wavelengths in a sequence from shortest (i.e., box name: Excitation No. 1) to longest wavelength into the dialogue boxes. Click OK to proceed; a new image window that presents a CRM image should pop up.</li>
		<li>Select the arbitrary background region (i.e., area where cells are absent) to be used for background subtraction. Draw a rectangle within the CRM image by mouse dragging. Double-click within the selected region to confirm the selection.</li>
		<li>Find the directory named Signature in the directory selected in step 5.2.4. 
		NOTE: The provided code automatically creates this directory. The &#8220;Signature&#8221; directory stores the innate fluorescence signature of each single microbial cell within a population as .png files that are serially numbered after a common prefix &#8220;Signature&#8221;.</li>
	</ol>
	</li>
</ol>
6. Statistical analysis
NOTE: Perform dimensional reduction techniques (e.g., principal component analysis [PCA]) to visualize the distribution of hyperspectrums of the cell populations. The provided script (PCA.py) executes PCA for two cell populations (i.e., two classes).
<ol>
	<li>Equip a workstation with the programming language and accompanying libraries and modules (Table of Materials).</li>
	<li>Create an empty directory in the C drive (or equivalent) and name the directory &#8220;Parent_directory&#8221; (i.e., C:/ Parent_ directory).</li>
	<li>Store the fluorescence signatures (e.g., the .png files generated in step 4.2.7) of each of the two cell populations into two separate directories. 
	NOTE: The two directories should be both located in the &#8220;Parent_directory&#8221;. 
	 
	C:/Parent_directory/ 
	<img alt="image 12" class="xfigimg" src="/files/ftp_upload/61120/61120img1.jpg" />putidaKT2440/ 
	<img alt="image 13" class="xfigimg" src="/files/ftp_upload/61120/61120img2.jpg" />&#160;<img alt="image 9" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;Signature01.png 
	<img alt="image 14" class="xfigimg" src="/files/ftp_upload/61120/61120img2.jpg" />&#160;<img alt="image 10" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;Signature02.png 
	<img alt="image 4" class="xfigimg" src="/files/ftp_upload/61120/61120img2.jpg" />&#160;<img alt="image 11" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;: 
	<img alt="image 5" class="xfigimg" src="/files/ftp_upload/61120/61120img1.jpg" />putidaKT2442/ 
	&#160;<img alt="image 6" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;Signature01.png 
	&#160;<img alt="image 7" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;Signature02.png 
	&#160;<img alt="image 8" class="xfigimg" src="/files/ftp_upload/61120/61120img3.jpg" />&#160;:</li>
	<li>Download PCA.py into the &#8220;Parent_directory&#8221;.</li>
	<li>Open the command line interface of the workstation.</li>
	<li>Type &#8220;python C:/Parent_directory/PCA.py&#8221; in the command line interface.</li>
	<li>Select the &#8220;Parent_directory&#8221; after the message &#8216;Select target directory&#8217; is displayed.</li>
	<li>In the &#8220;C:/Parent_directory&#8221;, find &#8220;PCA.png&#8221;, which contains a resulting PCA plot.&#160;</li>
</ol>

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The authors have nothing to disclose.

There are two critical points in this method that need to be closely followed to obtain reproducible results: 1) keep the laser power output under the microscope objective consistent through excitation wavelengths and experiments, and 2) perform accurate cell segmentation.
The first point is particularly important when comparing the innate fluorescence signature among different experiments. Avoid simply applying the same &#8220;percent output&#8221; settings to the excitation wavelengths (i.e....

Diverse biomolecules within a cell1 emit autofluorescence signals, and the innate fluorescence signature of a cell consists of the assembly of these signals. This signature fluorescence reflects various cellular properties and also differences in physiological status. Analysis of innate fluorescence is minimally invasive and can complement traditional, more invasive microbiological probes that leave a range of traces from mild metabolic modification to complete cell destruction. While traditional techniques such as DNA or cell content extraction2,3, fluorescent in situ hybridization4, and the introduction of fluorescent reporter genes to the genome are effective in determining cell type or physiological status, they commonly require either manipulation of the cells or invasive tagging.
Studies of the innate fluorescence of various live and intact microbial colonies, including bulk microbial culture suspensions5,6, active sludges7, mammalian tissues8,9, and mammalian cells1,10, have shown that innate fluorescence analysis facilitates tag-free analysis of cell types and physiological status. Innate fluorescence signatures have been traditionally analyzed at the population level and not at the single-cell level, and thus necessitate a clonal culture. In contrast, the confocal reflection microscopy-assisted single-cell innate fluorescence analysis (CRIF) technique11 described here reconstructs and catalogues the innate cellular fluorescence signature of each individual live microbial cell. Moreover, CRIF can systematically collate the innate fluorescence signature of a single microbial cell within a population that is distributed in a three-dimensional (3D) space.

<table border="0" cellpadding="0" cellspacing="0" style="width:720px;" width="720">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">Agarose</td>
			<td style="width:220px;">Wako Chemicals</td>
			<td style="width:119px;">312-01193</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="105">
			<td height="105" style="height:105px;width:215px;">Beam splitters</td>
			<td style="width:220px;">Carl Zeiss, Nikon</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">MBSInVis405, MBS458, MBS488, MBS458/514, MBS488/543, or MBS 488/543/633 beam splitters (Carl Zeiss)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:215px;">Confocal microscope</td>
			<td style="width:220px;">Carl Zeiss, Nikon</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">Model LSM 880 (Carl Zeiss), Model A1R (Nikon)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">Cover slips</td>
			<td style="width:220px;">Matsunami Glass</td>
			<td style="width:119px;">C024601</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">Glass slides</td>
			<td style="width:220px;">Matsunami Glass</td>
			<td style="width:119px;">S011120</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">Half-reflection mirror</td>
			<td style="width:220px;">Carl Zeiss, Nikon</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">NT80/20</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:215px;">Laser power meter</td>
			<td style="width:220px;">Thorlabs</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">PM400 (power meter console) and S175C (sensor)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">LB Broth</td>
			<td style="width:220px;">Nacalai tesque</td>
			<td style="width:119px;">20066-95</td>
			<td>For bacteria culture</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:215px;">Image analysis software</td>
			<td style="width:220px;">The MathWorks</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">MATLAB version 2019a or later, Image Processing Toolbox is needed</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:215px;">Microscope objective</td>
			<td style="width:220px;">Carl Zeiss, Nikon</td>
			<td style="width:119px;">440762-9904</td>
			<td style="width:167px;">e.g. 63x plan Apochomat NA = 1.4 (Carl Zeiss)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:215px;">Microscope software</td>
			<td style="width:220px;">Carl Zeiss, Nikon</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">ZEN (Carl Zeiss),NIS-elements (Nikon)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">PBS(-)</td>
			<td style="width:220px;">Wako Chemicals</td>
			<td style="width:119px;">166-23555</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="147">
			<td height="147" style="height:147px;width:215px;">Programming language</td>
			<td style="width:220px;"></td>
			<td style="width:119px;"></td>
			<td style="width:167px;">Python and libraries, modules (numpy, scikit-learn, scikit-image, os, glob, matplotlib, tkinter) are rquired to run the supplied PCA script.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">Silicone gasket</td>
			<td style="width:220px;">ThermoFisher Scientific</td>
			<td style="width:119px;">P24744</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:215px;">Workstation</td>
			<td style="width:220px;"></td>
			<td style="width:119px;"></td>
			<td style="width:167px;">A high-performance workstation with discrete GPUs is recommended.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:215px;">Yeast extract-peptone-dextrose (YPD) agar medium</td>
			<td style="width:220px;">Sigma-Aldrich</td>
			<td style="width:119px;">Y1500-250G</td>
			<td>For yeast culture</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:215px;">YPD medium</td>
			<td style="width:220px;">Sigma-Aldrich</td>
			<td style="width:119px;">Y1375-250G</td>
			<td></td>
		</tr>
	</tbody>
</table>

reconstruction of single-cell innate fluorescence signatures by confocal microscopy

Described here is confocal reflection microscopy-assisted single-cell innate fluorescence analysis (CRIF), a minimally invasive method for reconstructing the innate cellular fluorescence signature from each individual live cell in a population distributed in a three-dimensional (3D) space. The innate fluorescence signature of a cell is a collection of fluorescence signals emitted by various biomolecules within the cell. Previous studies established that innate fluorescence signatures reflect various cellular properties and differences in physiological status and are a rich source of information for cell characterization and identification. Innate fluorescence signatures have been traditionally analyzed at the population level, necessitating a clonal culture, but not at the single-cell level. CRIF is particularly suitable for studies that require 3D resolution and/or selective extraction of fluorescence signals from individual cells. Because the fluorescence signature is an innate property of a cell, CRIF is also suitable for tag-free prediction of the type and/or physiological status of intact and single cells. This method may be a powerful tool for streamlined cell analysis, where the phenotype of each single cell in a heterogenous population can be directly assessed by its autofluorescence signature under a microscope without cell tagging.

Figure 1A shows the typical single-cell fluorescence signature of a bacterial cell presented as a traditional spectrum plot (top) and as a heatmap (middle). Figure 1B shows the result of an accurate 2D cell segmentation superimposed over the original CRM image of a population of soil bacteria (Pseudomonas putida KT2440)12. The resulting innate fluorescence signatures for the population are presented as a heatmap in <strong class=...

Watch this Scientific Journal Video about Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy at JoVE.com

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Here, a protocol is presented for optically extracting and cataloging innate cellular fluorescence signatures (i.e., cellular autofluorescence) from every individual live cell distributed in a three-dimensional space. This method is suitable for studying the innate fluorescence signature of diverse biological systems at a single-cell resolution, including cells from bacteria, fungi, yeasts, plants, and animals.

Described here is confocal reflection microscopy-assisted single-cell innate fluorescence analysis (CRIF), a minimally invasive method for reconstructing ...

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Biology

2.7K Views.  University of Tsukuba. Here, a protocol is presented for optically extracting and cataloging innate cellular fluorescence signatures (i.e., cellular autofluorescence) from every individual live cell distributed in a three-dimensional space. This method is suitable for studying the innate fluorescence signature of diverse biological systems at a single-cell resolution, including cells from bacteria, fungi, yeasts, plants, and animals.

Video: Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Live Imaging of the Zebrafish Embryonic Brain by Confocal Microscopy

In this video, we demonstrate the method our lab has developed to analyze the cell shape changes and rearrangements required to bend and fold the developing zebrafish brain (Gutzman et al, 2008). Such analysis affords a new understanding of the underlying cell biology required for development of the 3D structure of the vertebrate brain, and significantly increases our ability to study neural tube morphogenesis. The embryonic zebrafish brain is shaped beginning at 18 hours post fertilization (hpf) as the ventricles within the neuroepithelium inflate. By 24 hpf, the initial steps of neural tube morphogenesis are complete. Using the method described here, embryos at the one cell stage are injected with mRNA encoding membrane-targeted green fluorescent protein (memGFP). After injection and incubation, the embryo, now between 18 and 24 hpf, is mounted, inverted, in agarose and imaged by confocal microscopy. Notably, the zebrafish embryo is transparent making it an ideal system for fluorescent imaging. While our analyses have focused on the midbrain-hindbrain boundary and the hindbrain, this method could be extended for analysis of any region in the zebrafish to a depth of 
80-100 &mu;m.

In this video, we demonstrate a method by which to analyze the developing vertebrate brain in live zebrafish embryos at single cell resolution by confocal microscopy. This includes the method by which we inject the single-cell zebrafish embryo and subsequently mount and image the developing brain.

In this video, we demonstrate the method our lab has developed to analyze the cell shape changes and rearrangements required to bend and fold the ...

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

Fluorescence-based Measurement of Store-operated Calcium Entry in Live Cells: from Cultured Cancer Cell to Skeletal Muscle Fiber

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to replenish depleted endoplasmic reticulum (ER) or sarcoplasmic reticulum (SR) Ca2+ stores1,2. Since Ca2+ is a ubiquitous second messenger, it is not surprising to see that SOCE plays important roles in a variety of cellular processes, including proliferation, apoptosis, gene transcription and motility. Due to its wide occurrence in nearly all cell types, including epithelial cells and skeletal muscles, this pathway has received great interest3,4. However, the heterogeneity of SOCE characteristics in different cell types and the physiological function are still not clear5-7.
The functional channel properties of SOCE can be revealed by patch-clamp studies, whereas a large body of knowledge about this pathway has been gained by fluorescence-based intracellular Ca2+ measurements because of its convenience and feasibility for high-throughput screening. The objective of this report is to summarize a few fluorescence-based methods to measure the activation of SOCE in monolayer cells, suspended cells and muscle fibers5,8-10. The most commonly used of these fluorescence methods is to directly monitor the dynamics of intracellular Ca2+ using the ratio of F340nm and F380nm (510 nm for emission wavelength) of the ratiometric Ca2+ indicator Fura-2. To isolate the activity of unidirectional SOCE from intracellular Ca2+ release and Ca2+ extrusion, a Mn2+ quenching assay is frequently used. Mn2+ is known to be able to permeate into cells via SOCE while it is impervious to the surface membrane extrusion processes or to ER uptake by Ca2+ pumps due to its very high affinity with Fura-2. As a result, the quenching of Fura-2 fluorescence induced by the entry of extracellular Mn2+ into the cells represents a measurement of activity of SOCE9. Ratiometric measurement and the Mn+2 quenching assays can be performed on a cuvette-based spectrofluorometer in a cell population mode or in a microscope-based system to visualize single cells. The advantage of single cell measurements is that individual cells subjected to gene manipulations can be selected using GFP or RFP reporters, allowing studies in genetically modified or mutated cells. The spatiotemporal characteristics of SOCE in structurally specialized skeletal muscle can be achieved in skinned muscle fibers by simultaneously monitoring the fluorescence of two low affinity Ca2+ indicators targeted to specific compartments of the muscle fiber, such as Fluo-5N in the SR and Rhod-5N in the transverse tubules9,11,12.

The extent of store-operated Ca2+ entry (SOCE) can be monitored using fluorescent Ca2+ indicators. Mn2+ quenching of such indicators assays SOCE in cultured cells and skeletal muscle fibers. A technique allowing spatial and temporal resolution of SOCE by confocal imaging of mechanically skinned muscle fibers is also described.

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to ...

In vivo Clonal Tracking of Hematopoietic Stem and Progenitor Cells Marked by Five Fluorescent Proteins using Confocal and Multiphoton Microscopy

We developed and validated a fluorescent marking methodology for clonal tracking of hematopoietic stem and progenitor cells (HSPCs) with high spatial and temporal resolution to study in vivo hematopoiesis using the murine bone marrow transplant experimental model. Genetic combinatorial marking using lentiviral vectors encoding fluorescent proteins (FPs) enabled cell fate mapping through advanced microscopy imaging. Vectors encoding five different FPs: Cerulean, EGFP, Venus, tdTomato, and mCherry were used to concurrently transduce HSPCs, creating a diverse palette of color marked cells. Imaging using confocal/two-photon hybrid microscopy enables simultaneous high resolution assessment of uniquely marked cells and their progeny in conjunction with structural components of the tissues. Volumetric analyses over large areas reveal that spectrally coded HSPC-derived cells can be detected non-invasively in various intact tissues, including the bone marrow (BM), for extensive periods of time following transplantation. Live studies combining video-rate multiphoton and confocal time-lapse imaging in 4D demonstrate the possibility of dynamic cellular and clonal tracking in a quantitative manner.

In vivo Clonal Tracking of Hematopoietic Stem and Progenitor Cells Marked by Five Fluorescent Proteins using Confocal and Multiphoton Microscopy

Combinatorial 5 fluorescent proteins marking of hematopoietic stem and progenitor cells allows in vivo clonal tracking via confocal and two-photon microscopy, providing insights into bone marrow hematopoietic architecture during regeneration. This method allows non-invasive fate mapping of spectrally-coded HSPCs-derived cells in intact tissues for extensive periods of time following transplantation.

We developed and validated a fluorescent marking methodology for clonal tracking of hematopoietic stem and progenitor cells (HSPCs) with high spatial and ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Detection of Ligand-activated G Protein-coupled Receptor Internalization by Confocal Microscopy

Confocal laser scanning microscopy (CLSM) is an optical imaging technique for high-contrast imaging. It is a powerful approach to visualize fluorescent fusion proteins, such as green fluorescent protein (GFP), to determine their expression, localization, and function. The subcellular localization of target proteins is important for identification, characterization, and functional analyses. Internalization is one of the predominant mechanisms controlling G protein-coupled receptor (GPCR) signaling to ensure the appropriate cellular responses to stimuli. Here, we describe an experimental method to detect the subcellular localization and internalization of GPCR in HEK293 cells with confocal microscopy. In addition, this experiment provides some details about cell culture and transfection. This protocol is compatible with a variety of widely available fluorescent markers and is applicable to the visualization of the subcellular localization of a majority of proteins, as well as of the internalization of GPCR. This technique should enable researchers to efficiently manipulate GPCR gene expression in mammalian cell lines and should facilitate studies on GPCR subcellular localization and internalization.

This protocol describes confocal microscopy detection of G protein-coupled receptor (GPCR) internalization in mammalian cells. It includes the basic cell culture, transfection, and confocal microscopy procedure and provides an efficient and easily interpretable method to detect the subcellular localization and internalization of fusion-expressed GPCR.

Confocal laser scanning microscopy (CLSM) is an optical imaging technique for high-contrast imaging. It is a powerful approach to visualize fluorescent ...

Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can be used to visualize and quantify competitive interactions between different bacterial strains at the single-cell level. The protocol described here provides methods for advanced approaches in slide preparation on both upright and inverted epifluorescence microscopes, live-cell and time-lapse imaging techniques, and quantitative image analysis using the open-source software FIJI. The approach in this manuscript outlines the quantification of competitive interactions between symbiotic Vibrio fischeri populations by measuring the change in area over time for two coincubated strains that are expressing different fluorescent proteins from stable plasmids. Alternative methods are described for optimizing this protocol in bacterial model systems that require different growth conditions. Although the assay described here uses conditions optimized for V. fischeri, this approach is highly reproducible and can easily be adapted to study competition among culturable isolates from diverse microbiomes.

This manuscript describes a method for using single-cell fluorescence microscopy to visualize and quantify bacterial competition in coculture.

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can ...

Autofluorescence Imaging to Evaluate Red Algae Physiology

Red algae (Rhodophyta) contain phycobiliproteins and colonize habitats with dim light, however some (e.g., some Chroothece species) can also develop in full sunshine. Most rhodophytes are red, however some can appear bluish, depending on the proportion of blue and red biliproteins (phycocyanin and phycoerythrin). Different phycobiliproteins can capture light at diverse wavelengths and transmit it to chlorophyll a, which makes photosynthesis under very different light conditions possible. These pigments respond to habitat changes in light, and their autofluorescence can help to study biological processes. Using Chroothece mobilis as a model organism and the spectral lambda scan mode in a confocal microscope, the adaptation of photosynthetic pigments to different monochromatic lights was studied at the cellular level to guess the species' optimal growth conditions. The results showed that, even when the studied strain was isolated from a cave, it adapted to both dim and medium light intensities. The presented method is especially useful for studying photosynthetic organisms that do not grow or grow very slowly under laboratory conditions, which is usually the case for those living in extreme habitats.

The present protocol describes step-by-step autofluorescence imaging and evaluation of phycobiliprotein changes in red algae based on spectral analysis. This is a label-free and non-destructive method to evaluate cellular adaptation to extreme habitats, when only scarce material is available and cells grow slowly, or not at all, under laboratory conditions.

Red algae (Rhodophyta) contain phycobiliproteins and colonize habitats with dim light, however some (e.g., some Chroothece species) can also develop in ...

Isolation and Transcriptome Analysis of Plant Cell Types

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, which is known as cellular heterogeneity. In recent years, single-cell and cell-type isolation combined with next-generation sequencing (NGS) techniques have become important tools for studying biological processes at single-cell resolution. However, isolating plant cells is relatively more difficult due to the presence of plant cell walls, which limits the application of single-cell approaches in plants. This protocol describes a robust procedure for fluorescence-activated cell sorting (FACS)-based single-cell and cell-type isolation with plant cells, which is suitable for downstream multi-omics analysis and other studies. Using Arabidopsis root fluorescent marker lines, we demonstrate how particular cell types, such as xylem-pole pericycle cells, lateral root initial cells, lateral root cap cells, cortex cells, and endodermal cells, are isolated. Furthermore, an effective downstream transcriptome analysis method using Smart-seq2 is also provided. The cell isolation method and transcriptome analysis techniques can be adapted to other cell types and plant species and have broad application potential in plant science.

The feasibility and effectiveness of high-throughput scRNA-seq methods herald a single-cell era in plant research. Presented here is a robust and complete procedure for isolating specific Arabidopsis thaliana root cell types and subsequent transcriptome library construction and analysis.

In multicellular organisms, developmental programming and environmental responses can be highly divergent in different cell types or even within cells, ...

Imaging of Mitochondrial Peroxide in Living Yeast Cells Using mtHyper7

Imaging of mtHyPer7, a Ratiometric Biosensor for Mitochondrial Peroxide, in Living Yeast Cells

Mitochondrial dysfunction, or functional alteration, is found in many diseases and conditions, including neurodegenerative and musculoskeletal disorders, cancer, and normal aging. Here, an approach is described to assess mitochondrial function in living yeast cells at cellular and subcellular resolutions using a genetically encoded, minimally invasive, ratiometric biosensor. The biosensor, mitochondria-targeted HyPer7 (mtHyPer7), detects hydrogen peroxide (H2O2) in mitochondria. It consists of a mitochondrial signal sequence fused to a circularly permuted fluorescent protein and the H2O2-responsive domain of a bacterial OxyR protein. The biosensor is generated and integrated into the yeast genome using a CRISPR-Cas9 marker-free system, for&#160;more consistent expression compared to plasmid-borne constructs.
mtHyPer7 is quantitatively targeted to mitochondria, has no detectable effect on yeast growth rate or mitochondrial morphology, and provides a quantitative readout for mitochondrial H2O2 under normal growth conditions and upon exposure to oxidative stress. This protocol explains how to optimize imaging conditions using a spinning-disk confocal microscope system and perform quantitative analysis using freely available software. These tools make it possible to collect rich spatiotemporal information on mitochondria both within cells and among cells in a population. Moreover, the workflow described here can be used to validate other biosensors.

Author Spotlight: Advancing Mitochondrial Research - mtHyper7 Biosensor for Subcellular Analysis 

Hydrogen peroxide (H2O2) is both a source of oxidative damage and a signaling molecule. This protocol describes how to measure mitochondrial H2O2 using mitochondria-targeted HyPer7 (mtHyPer7), a genetically encoded ratiometric biosensor, in living yeast. It details how to optimize imaging conditions and perform quantitative cellular and subcellular analysis using freely available software.

Mitochondrial dysfunction, or functional alteration, is found in many diseases and conditions, including neurodegenerative and musculoskeletal disorders, ...