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Providing researchers single cell sequencing with unprecedented scale and ease

About Parse

Deciding Between RT and Probe-Based Single Cell?

Probe-based and reverse transcription (RT) workflows measure gene expression differently. Before choosing a platform, understand how each approach can affect what biology you see.

One Sample, Two Stories

Same Samples.
Different Readouts.

RT-based and probe-based workflows do not measure gene expression the same way. That can affect whether signals appear present, absent, inflated, or biologically meaningful.

Probe-Based Measurement
RT-Based Whole Transcriptome Capture
Limited to selected gene regions
Captures RNA more broadly
Results shaped on probe design
Less dependent on target probe design
Can limit discovery outside selected regions
Supports broader discovery
May flatten expression differences
More accurately reflects gene expression levels

What Can Change?

The way RNA is measured can change how gene expression appears across cells. Differences in capture strategy can affect whether genes appear absent, inflated, or differentially expressed.

A Real Signal Can Look Absent

If a probe does not capture a transcript well, biology may appear missing even when it is present.

The Effect is Larger Than a Single Marker

Across genes, probe-based measurement can change what appears present, absent, or differentially expressed.

More Expression May Just Mean More Probes

Probe-based assays can assign different numbers of probes to different genes. If counts are summed across probes, genes with more probes can appear more highly expressed, limiting differential gene expression conclusions.

Artifacts Can Look Meaningful

Unexpected signal can suggest a cell state that may not be real.

Not All Expression Data Is Created Equal

What to Consider Before Choosing a Platform

RT-based and probe-based workflows do not measure gene expression the same way. That can affect whether signals appear present, absent, inflated, or biologically meaningful.

Is your study targeted or discovery driven?

Focused study designs can be valuable when you know which biology you want to investigate. The key question is whether that focus comes at the cost of broader gene coverage or transcript context.

How will measurement method affect expression values?

Probe number, binding efficiency, and target region selection can influence probe-based signal. If your study depends on gene expression comparisons, make sure the readout reflects transcript abundance, not assay design.

Could unexpected biology matter?

If your study depends on finding unknown, subtle, or cell type-specific signals, measurement method matters. A targeted approach may miss biology that falls outside the probe design or make it harder to interpret signals that appear assay-specific.

How important is confidence in unexpected signals?

Unexpected signals can be valuable, but only if they reflect true biology. If a signal appears in one method and not another, it can be harder to tell whether it reflects the sample or the measurement approach.
Full Transcriptome. Full Confidence.

Measure Biology Without Compromise

Evercode WT uses reverse transcription chemistry enabling whole transcriptome profiling. This gives researchers a scalable way to study gene expression without relying on probe-based detection.

With Parse, researchers can scale single cell studies while keeping broad transcriptome visibility, flexible sample handling, and discovery potential.

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