Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Summary

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.

Abstract

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.

Introduction

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 extraction^2,3, 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 suspensions^5,6, active sludges⁷, mammalian tissues^8,9, and mammalian cells^1,10, 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.

Protocol

1. Preparation of the sample

1. Place a 1 mm thick silicone gasket with wells on a glass slide.
2. Place a 1 mm thick 0.8% (w/v) agarose slab in the well of the silicone gasket.
3. Dilute the cell density of an arbitrary microbial cell culture to an optical density at 600 nm (OD₆₆₀) = 1.0.
4. Place a 5 µL aliquot of cell suspension on the agarose slab.
5. Cover gently with a glass coverslip.

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.

1. Connect a confocal microscope with descanned spectral channels to a photomultiplier tube (PMT) or GaAsP detector.
   NOTE: These setups are available from several manufacturers.
2. Equip the microscope with a high numerical aperture (NA) objective with adequate magnification.
   NOTE: A 63x objective with NA > 1.4 is recommended for analyzing bacterial cells.
3. 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.
4. 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.
5. 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 µW with the 63x objective).

3. Image acquisition

1. Set the pinhole size to 1 AU using the microscope software.
2. 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 <55.6 µs/µm² (when the irradiance output under the microscope is ~17 µW/cm²) is usually suitable to avoid growth inhibition. This parameter may vary depending on the organisms and experimental setups.
3. Set the scanning resolution. For small cells such as bacteria, use a scanning area of 1,024 x 1,024.
4. 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 µm is usually sufficient.
5. Set the descanned detector to capture the visible wavelength range (e.g., 416−691 nm). Use a spectral window of 8−10 nm.
6. Acquire multichannel confocal microspectroscopy images in a sequence from longest to shortest excitation wavelengths to create Z-stacks of fluorescence images.
7. Acquire CRM images.
8. 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 “c” and “z” are prefixes for the detector number and the Z-slice number, respectively.
   1. For example, if a multichannel confocal microspectroscopy image is taken with an excitation wavelength of 405 nm, 1^st detector channel of the detector array, and is the 5^th slice of the Z-stack, name it “405c01z05.tif”. For CRM images, use the string “CRM” in place of XXX (e.g., “CRMc01z05.tif”).
      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.

4. 2D image analysis

1. Equip a workstation with image analysis software (e.g., MATLAB).
2. Perform cell segmentation and reconstruction of single-cell innate fluorescence signatures.
   1. Open the image analysis software.
   2. Double-click and open one of the provided scripts “Script2D.m”.
   3. Go to the Editor tab, then click Run. A folder selection window should appear.
   4. 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 (“Sample_2D”). The sample dataset is provided as a compressed file and should be extracted in advance.
   5. Input the segmentation parameters: Threshold of Image Binarization (0−1) 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.
   6. 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.
   7. 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 “Signature” directory stores the innate fluorescence signature of each microbial cell within a population as .png files that are serially numbered after a common prefix “Signature”.

5. 3D image analysis

1. Equip a workstation with the image analysis software (Table of Materials).
2. Perform cell segmentation and reconstruction of single-cell innate fluorescence signatures.
   1. Open the image analysis software.
   2. Double-click and open the provided script “Script3D.m”.
   3. Go to the Editor tab, then click Run. A folder selection window should appear.
   4. 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 (“Sample_3D”). The sample dataset is provided as a compressed file and should be extracted in advance.
   5. Input the segmentation parameters: Threshold of Image Binarization (0–1) 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 [μm/pixel] = 0.26, Y Pixel Size [μm/pixel] = 0.26, Z pixel Size [μm/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.
   6. 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.
   7. 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.
   8. 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.
   9. Find the directory named Signature in the directory selected in step 5.2.4.
      NOTE: The provided code automatically creates this directory. The “Signature” 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 “Signature”.

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).

1. Equip a workstation with the programming language and accompanying libraries and modules (Table of Materials).
2. Create an empty directory in the C drive (or equivalent) and name the directory “Parent_directory” (i.e., C:/ Parent_ directory).
3. 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 “Parent_directory”.
   
   C:/Parent_directory/
   putidaKT2440/
     Signature01.png
     Signature02.png
     :
   putidaKT2442/
     Signature01.png
     Signature02.png
     :
4. Download PCA.py into the “Parent_directory”.
5. Open the command line interface of the workstation.
6. Type “python C:/Parent_directory/PCA.py” in the command line interface.
7. Select the “Parent_directory” after the message ‘Select target directory’ is displayed.
8. In the “C:/Parent_directory”, find “PCA.png”, which contains a resulting PCA plot. 

Results

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

Discussion

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 “percent output” settings to the excitation wavelengths (i.e....

Materials

List of materials used in this article

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