Standard DNA-based 16S rRNA gene sequencing detects bacterial marker-gene DNA; it cannot by itself tell whether the bacteria that contributed that DNA were alive. This matters when interpreting microbiome samples collected after antimicrobial treatment, environmental stress, or changes in host health.

Figure 1. Conventional 16S rRNA gene sequencing detects bacterial DNA from membrane-intact cells, membrane-compromised cells, and extracellular sources. Additional methods, including PMA-based PCR, culture, flow cytometry, and RNA analysis, provide complementary information about microbial viability or activity but measure different biological properties. This conceptual figure was generated by AI for Microbes World.

What does 16S rRNA sequencing actually detect?

The assay amplifies regions of the bacterial 16S ribosomal RNA gene from extracted DNA. Sequencing identifies marker-gene sequences, not individual cells; extraction and amplification biases and variation in 16S gene copy number also complicate quantitative interpretation.

The problem is not unique to 16S rRNA gene sequencing. Conventional DNA-based shotgun metagenomics can also recover genetic material from nonviable microorganisms.

Additional methods are needed when the question concerns microbial viability, activity or recoverable cell numbers. These methods provide different kinds of evidence; none is a universal measurement of living bacteria.

Why DNA from dead bacteria remains detectable

When bacterial cells lose viability, their membranes may become damaged and their contents may eventually be released into the surrounding environment. DNA can persist outside cells, particularly when environmental conditions slow its degradation.

Extracellular DNA originating from previously living microorganisms is often described as relic DNA. Depending on the context, researchers also consider DNA retained within nonviable or membrane-compromised cells when assessing the nonliving contribution to a sequencing profile.

The persistence of this material varies considerably. Temperature, moisture, microbial enzymes, ultraviolet radiation and the chemical composition of the environment can influence how quickly DNA is degraded.

In soils and sediments, DNA may associate with mineral particles or organic material, potentially protecting it from degradation. Biofilm matrices and other structured environments may also influence its persistence.

During conventional DNA extraction, genetic material from several sources can enter the same sequencing library. PCR primers do not determine whether a particular 16S rRNA gene originated from a bacterium that was alive when the sample was collected.

As a result, a taxon may appear in a sequencing dataset even when some or all of its detected DNA originates from nonviable cells.

Viability, membrane integrity and culturability are different

A membrane-intact bacterium retains a physical barrier between its cytoplasm and the surrounding environment. Membrane integrity is useful for assessing cellular damage, but it does not establish that the cell is metabolically active or capable of reproduction.

A metabolically active bacterium is carrying out measurable biochemical processes, such as respiration, substrate utilization or transcription. Activity can change substantially with environmental conditions.

A culturable bacterium is capable of producing detectable growth under the laboratory conditions selected for an experiment. Colony-forming units, or CFU, estimate the number of units capable of forming colonies on the chosen medium.

Some bacteria enter dormant states and remain viable without actively multiplying. Others may retain cellular activity but fail to form colonies under the conditions used for routine cultivation. This is particularly relevant to the concept of viable but nonculturable (VBNC) cells.

Conversely, a cell may retain an apparently intact membrane after losing the ability to recover or reproduce.

Therefore, describing a measurement as a live bacterial count requires specifying how viability was defined and assessed.

How much can relic DNA change microbiome results?

In soil samples, Carini and colleagues (2017) reported that relic DNA could account for a substantial portion of the recovered DNA pool—approximately 40% on average in the soils examined. Removing this contribution changed some estimates of richness and community composition. The figure is not a universal correction factor for other environments.

Lennon and colleagues subsequently combined ecological models with 16S rRNA analyses of 34 samples from soil, sediment, water and mammalian gut environments. They estimated that relic DNA contributed an average of 33% of the total bacterial DNA pool, with substantial variation between samples.

Their modelling indicated that bias depends not only on the amount of relic DNA but also on how its taxonomic composition differs from that of the intact-cell DNA pool.

These results are informative precisely because they differ. The consequences of relic DNA depend on the size and taxonomic composition of the relic pool relative to the intact-cell DNA pool. One should not assume that removing relic DNA will always change richness or diversity in a predictable direction.

How PMA treatment works

Propidium monoazide (PMA) is a photo-reactive DNA-binding dye. The method takes advantage of differences in membrane permeability. Under suitable conditions, PMA can enter cells with compromised membranes and interact with their DNA. It can also bind accessible extracellular DNA.

After exposure to an appropriate light source, PMA forms covalent attachments that inhibit subsequent amplification of the affected DNA.

DNA inside membrane-intact cells is generally less accessible to the dye. Consequently, PMA treatment before DNA extraction can reduce the contribution of membrane-compromised cells and accessible extracellular DNA to a PCR-based analysis.

In a PMA-16S workflow, a treated sample is often compared with an untreated sample to examine differences in the detected bacterial community.

However, the result should be described carefully. PMA treatment primarily provides information related to membrane integrity and DNA accessibility.

Why PMA requires validation

Bacterial species differ in cell-envelope characteristics. Some treatments damage bacterial physiology without immediately disrupting membrane integrity. Other treatments may alter DNA accessibility or interfere with subsequent amplification.

Sample composition also matters. Turbid or highly complex samples can affect light exposure, dye accessibility and assay performance. Dye concentration, sample processing, PCR target length and the detection limit of the amplification method can influence the results.

Kaur and colleagues (2025) tested PMA performance in molecular assays using mixtures containing Escherichia coli O157:H7, Salmonella enterica and Listeria monocytogenes. Their experiments addressed how bacterial concentration and mixture composition affect PMA-mediated exclusion of amplifiable DNA.

They found that the dye was not a dependable quantitative live–dead discriminator when the composition and concentration of a bacterial mixture were unknown. This result cautions against treating PMA-qPCR values as exact viable-cell numbers in unvalidated samples.

These findings came from specific laboratory molecular assays; they should not be converted into a blanket claim that every PMA-16S protocol fails.

What a 2025 seawater study demonstrated

In 2025, Thomas and colleagues evaluated a PMA-assisted sequencing workflow in natural seawater and laboratory-treated mixtures. The researchers combined PMA treatment, 16S rRNA gene sequencing, droplet digital PCR and flow cytometry.

Their experiments included natural seawater, heat-treated communities and mixtures containing defined proportions of natural and heat-treated material. This allowed them to evaluate whether the workflow reflected expected changes in the proportion of membrane-intact cells.

The investigators evaluated PMA concentrations from 2.5 to 15 µM in their seawater workflow and reported a reduction of approximately 24–44% in measured 16S rRNA gene copies after treatment in the tested samples. Those percentages describe their experiment, not the proportion of dead cells in all seawater communities.

Relative sequencing profiles describe the proportions of detected taxa. They may fail to reveal that the overall microbial load has declined.

By incorporating independent microbial-load measurements, the researchers could better represent the direction and magnitude of abundance changes in their experimental seawater communities.

The findings do not establish that the same PMA concentrations or procedures will perform equally well in faecal samples, soil, oral biofilms or other complex environments.

Why PMA and culture results may disagree

Schwendener and colleagues (2025) studied a five-species oral biofilm model. The researchers grew multispecies biofilms on hydroxyapatite discs and exposed them to chlorhexidine or sodium hypochlorite under defined experimental conditions.

They compared culture-based colony counts with molecular measurements obtained with and without PMA treatment.

Following certain treatments, PMA-qPCR measurements were reasonably consistent with culture-based results. However, important discrepancies remained.

For Fusobacterium nucleatum, PMA-qPCR gave higher estimates than colony counting following a chlorhexidine treatment. After some disinfectant exposures, no colonies were recovered although PMA-treated samples still yielded amplifiable DNA.

Sodium hypochlorite also affected DNA directly, complicating interpretation of PCR results.

Culture and PMA measure different properties: one assesses colony formation under specified conditions, while the other assesses DNA accessibility under a membrane-integrity-based treatment.

Can RNA reveal active bacteria?

RNA offers a different perspective from DNA. Unlike DNA, many RNA molecules are associated with ongoing or relatively recent cellular processes. Messenger RNA can offer information about which genes are being transcribed under particular conditions.

Metatranscriptomics is therefore useful for studying microbial gene expression within a community.

Ribosomal RNA can persist in dormant cells, and the amount of rRNA does not consistently correspond to growth rate across different organisms or physiological states.

Messenger RNA is generally less stable, but its interpretation also depends on sample preservation, extraction efficiency, RNA degradation and the timing of sampling.

For example, a taxon may be abundant in a DNA profile but contribute relatively little to the measured transcript pool. Differences in transcriptional activity are one possible explanation, although RNA stability and other technical biases must also be considered.

The appropriate method depends on the biological question. The comparison below summarizes what each approach can—and cannot—measure.

Choosing the right method for a research question

Culture, membrane-integrity assays, flow cytometry and RNA measurements answer related but different questions. A method should be chosen according to the biological endpoint rather than the convenience of a single “live/dead” label.

For an experiment investigating the effects of a disinfectant, antibiotic or environmental stressor on bacterial survival, an additional viability-related measurement may be necessary.

For a study examining metabolic responses, transcriptomic or other activity-oriented methods may be more informative.

Relative abundance is not a cell count

Consider a simplified hypothetical example, not experimental data. A microbial community contains two bacterial groups, A and B.

At the first sampling point, each group contributes half of the total bacterial population. At the second sampling point, both populations decline tenfold.

Their relative proportions could remain 50% and 50%, even though the number of cells belonging to each group has fallen substantially.

Methods such as flow cytometry or quantitative PCR can provide additional information about microbial load, although gene copy-number variation, extraction efficiency and other technical biases still need consideration.

For further background, see Microbial Communities in the Human Body, which explains why microbiome read proportions require careful interpretation.

Controls that make a viability study interpretable

For PMA-based measurements, this commonly means comparing matched treated and untreated samples and including suitable membrane-intact and membrane-compromised controls.

Where feasible, defined mixtures can help evaluate whether the method responds appropriately to known changes in sample composition.

Extraction blanks and amplification controls are needed to investigate contamination and technical failure. Independent culture or cell-count measurements may help evaluate whether the molecular assay is behaving as expected.

The choice of controls should reflect the sample matrix, organism and biological question. A protocol validated using a simple bacterial suspension should not automatically be transferred to a complex faecal or environmental sample without additional testing.

Results should name the measured property precisely. For example, “DNA remaining amplifiable after PMA treatment” is more defensible than “living bacteria,” and “colony-forming units” is more defensible than “all viable cells.”

For related fundamentals, read Bacterial Characteristics on Microbes World.

Interpreting sequencing findings without overclaiming

If conventional 16S rRNA gene sequencing detects a bacterial taxon, the defensible conclusion is that the corresponding marker-gene DNA was present in the sample.

Similarly, detecting a microbial gene does not prove that the gene was expressed or that its encoded biological function occurred.

A well-designed study can combine DNA sequencing with culture, membrane-integrity measurements, microbial-load estimates or activity-based assays to answer more specific questions.

The practical conclusion is to match the claim to the measurement. Standard 16S sequencing detects bacterial marker-gene DNA; PMA treatment evaluates DNA accessibility under defined conditions; culture measures colony-forming ability; and RNA-based methods provide evidence related to gene expression. None of these measurements alone describes every aspect of bacterial viability.

References