Single-Cell Microbiology

Summary Points

• Current lab methods for bacterial analysis are based on bacterial culturing, which consists of extensive growth on artificial nutritional media.
• They also require strain isolation, which requires at least one additional growth step.
• These requirements impose delays of many hours, at a minimum. In the best case, results are not available until the next day. But culturing typically requires two to three days.
• Single-cell analysis is a new concept that could yield new methods to replace culturing and isolation. But such methods have not yet developed into practical diagnostic applications.
• Accelr8 is developing a new set of single-cell microbiology methods that form the basis of the BACcelr8r™, naming the process “Quantum Microbiology™” (QM) to emphasize the single-cell strategy.
• The new methods apply well-known, accepted test principles but in forms selected for single-cell analysis and readout by an automated microscope.
• Laboratory studies are showing that QM methods are practical, rapid, and highly sensitive.

Limitations of Bacterial Culturing

Standard clinical microbiology analyses now rely on bacterial culturing and colony isolation. Even automated instruments for bacterial identification (ID) and antibiotic susceptibility testing (AST) require samples derived from bacteria grown in culture followed by strain isolation. They can only use purified isolates. Current methods also require millions of cells to be able to detect bacterial responses.

Culturing emerged in the 19^th Century from Pasteur's pioneering work. In the 1870s and 1880s Robert Koch developed the basic techniques that form the basis of today's practices. Culturing and colony isolation proved very powerful and made modern microbiology possible. Its methods are usually simple and low cost, and yield highly reproducible results.

The first step with culturing is to grow a very large number of bacteria. Each individual, original bacterial cell from a sample (a progenitor cell) must grow into tens or hundreds of millions of descendent cells. As the descendent line grows, the cells form a discrete mass that eventually becomes a visible colony on the surface of an agar plate (a Petri dish containing a nutrient gel). Colonies from a clinical specimen typically grow into specks a couple of millimeters in diameter.

This initial step usually requires at least overnight growth. After growing colonies on a plate, the microbiologist then physically picks a small number (typically 3-7) of individual colonies that appear identical. After being mixed in a nutrient broth, this “isolate” then provides the starting material for various tests.

This is a critical step. Tests that use culture methods require pure strains (isolates). Test design is based on the assumption that all bacteria in a test have identical genomes (identical DNA sequences). Mixtures of even a small number of “contaminating” cells can invalidate the results. When running tests, microbiologists also run another culture plate to verify isolate purity.

Most bacterial tests produce a response that is visible to the unaided eye. Many such assays require further bacterial growth as part of their process. They have low sensitivity and therefore need millions of bacteria to produce a readable signal. Because of these growth requirements, a total culture-based analysis typically consumes two to three days.

Standard analyses only work with isolates. When microbiologists pick colonies for analysis, they ignore colonies that are only few in proportion. Since culture plates can be easily contaminated, microbiologists try not to waste resources on possible contaminants. But this judgment itself introduces error because almost half of serious invasive infections involve multiple strains or species of pathogen. Subjective colony selection can miss clinically important variants.

Manual methods used for culturing and isolation thus introduce a significant risk of sample bias. If it were practical, a better method would be to build a bacterial population model from analyses of each individual cell in a specimen. Single-cell analyses could also eliminate the long delays inherent in culturing and isolation – providing that the analyses themselves are quick.

Potential Advantages of Single-Cell Analysis

The idea of single-cell bacterial analysis harkens back to van Leeuwenhoek's discovery of bacteria in 1676 using a home-made 275x single-lens microscope. However, the direct study of individual infectious bacteria by microscope would be very time consuming and would not yield the information needed for accurate identification or antibiotic testing.

In concept, single-cell microbiology could be both powerful and simple: powerful by including all cells in a sample, and simple in counting individual cell-level, yes/no events. Automated digital research microscopes with image analysis software could replace manual observation. This is already done commercially for certain types of tissue examination. The hurdle is to devise assays (tests) for the bacterial properties needed to perform a laboratory diagnosis.

Implementation with Quantum Microbiology™

We are developing a proprietary set of single-cell microbiology methods that we call Quantum Microbiology™ (or “QM”). The “Quantum” label simply emphasizes its analysis of each cell as a unique individual entity.

In a nutshell, QM uses familiar, robust analytical assays and scales them down to the single-cell level. We select methods that provide clear visual responses at the microscopic scale.

QM analyzes thousands of individual cells from a single patient specimen. It performs a series of tests over time, counting the number of cells and recording the time intervals in which they exhibit a response. It ends by statistically compiling a population model for the whole sample. During the series of analyses, QM reports results at key points, such as species identification. It provides actionable information as soon as it becomes available.

QM differs from most other research methods for single-cell analysis – it immobilizes individual cells so the microscope can return time after time to the same identified individual cell as each test progresses. QM assigns a unique “name and address” or the equivalent of “micro-scale GPS coordinates” to each individual cell.

The following table summarize the QM sequence. Elapsed time begins when the raw specimen begins processing. Times vary with different pathogens and their responses to different assays and antimicrobial agents –

Step	Description	Time (approx)
Sample prep	Brief manual cleanup to reduce interfering materials in the patient specimen. Produces a sample for analysis.	30 min.
Capture	Electrokinetic field forces bacteria to a surface. The surface has a coating that binds to the bacteria, immobilizing them in fixed locations for subsequent analyses. Eliminates culturing and isolation. Growth begins at end of this step.	+5 min (35 min)
Identification	Identifies Gram type, cell morphology type, reaction to antibodies against particular species, and other tests for particular species.	+40 min (75 min)
Growth	Additional time for growth of cells or clones, if needed. Permits identification of actively growing individual cells for accurate counting and growth rate measurement.	+30 min (105 min) Report
Prelim Antibiotics	Rough identification of major categories of antimicrobial resistance. Used to improve identification (antibiotype).	+60-120 min (165-225 min) Report
Final Antibiotics	Detailed analysis of antibiotic susceptibility using a large library of antibiotics and other antimicrobial agents.	+1-4 hrs (~4-8 hrs) Report

For more details and examples of experimental demonstrations, go to the Quantum Microbiology™ page.

Recommended Reading

An excellent recent (2004) review of single-cell methods in microbiology is available free online –

Brehm-Stecher BF, and Johnson EA.
“Single-Cell Microbiology: Tools, Technologies, and Applications.”
Microbiol Mol Biol Rev. 2004 Sep;68(3):538-559

Abstract

The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly "clonal" populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the "averaging" effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail.

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A current review of theoretical and experimental effects of cell individuality appears in the August, 2006 issue of Nature Reviews Microbiology (subscription may be required) –

Avery SV.
“Microbial cell individuality and the underlying sources of heterogeneity.”
Nature Reviews Microbiology 4: 577-587 (August 2006)

Abstract

Single cells in genetically homogeneous microbial cultures exhibit marked phenotypic individuality, a biological phenomenon that is considered to bolster the fitness of populations. Major phenotypes that are characterized by heterogeneity span the breadth of microbiology, in fields ranging from pathogenicity to ecology. The cell cycle, cell ageing and epigenetic regulation are proven drivers of heterogeneity in several of the best-known phenotypic examples. However, the full contribution of factors such as stochastic gene expression is yet to be realized.