LAMP Primer Design Explained: Six Primers, One Temperature

Have you ever designed a PCR primer pair in five minutes and felt rather proud of yourself? Enjoy that feeling. LAMP is about to take it away.

LAMP stands for loop-mediated isothermal amplification, and it asks you to design six primers for one target, not two. Yet people love it, because it needs no thermocycler, gives positives in under 30 minutes and works in places where a PCR machine would never survive the trip. So how do you design those six primers without losing your mind? Let's go through it, term by term.

A short story about a very stubborn enzyme

In 2000, Tsugunori Notomi and colleagues at Eiken Chemical in Japan published a method that copied DNA at a single constant temperature (Notomi et al., 2000). That is what isothermal means: one temperature for the whole reaction, usually somewhere between 60 and 72 °C. A heat block or a water bath will do.

PCR needs a thermocycler because every cycle starts with denaturation, which means heating the DNA to about 95 °C so the two strands come apart. LAMP skips this step, and the reason is its enzyme.

LAMP uses a polymerase with strong strand displacement activity, such as Bst polymerase from Geobacillus stearothermophilus. Most polymerases stop, or chew up the DNA, when they meet double-stranded DNA ahead of them. A strand-displacing polymerase behaves like a snowplow. It keeps going and pushes the downstream strand off the template as it copies. In practice, that is why no heat cycling is needed, and why every new copy releases a single strand that can be primed again.

How does the reaction start without heating? At 60 to 65 °C the ends of double-stranded DNA keep opening and closing for a moment, which is called "breathing". An inner primer can slip into an open end, a step called strand invasion, and the polymerase takes over from there.

Meet the six primers

Here is the cast. Each name looks like alphabet soup until you see what the primer actually does.

• F3 and B3 (outer primers): the forward and backward primers sitting furthest out on the target. Their job is to displace the strand made by the inner primers, which releases it for the next step. They are used at the lowest concentration.
• FIP and BIP (forward and backward inner primers): the stars of the show. They are long, 40 to 50 nucleotides, because each one is two pieces joined together. One piece (F2 or B2) matches the target. The other (F1c or B1c) is the reverse complement of a region further inside the target. The "c" means complementary. That second piece is what lets the product fold back on itself.
• LF and LB (loop primers): optional, but I would not skip them. They bind the single-stranded loops between F2 and F1c, and between B2 and B1c. They give the polymerase more places to start, so the reaction speeds up. The webinar transcript calls them "blue primers", which is a speech-to-text mishearing of "loop primers". Nagamine and colleagues showed that loop primers accelerate the reaction (Nagamine et al., 2002).

Together, these primers cover a stretch of target called the amplicon footprint. For six primers you need about 280 base pairs or more, and you must leave a gap between F2 and F1c so that the loop primer fits there.

Why LAMP products look like a bowl of spaghetti

Once the inner primer has extended and the outer primer has displaced the new strand, that strand has complementary sequences at both ends. So it folds into a stem-loop, also called a dumbbell: each end pairs with a region within the same strand. The folded 3' end then acts as its own primer, and the strand extends itself with no new primer needed. This is the engine of LAMP.

The result is concatemers: long molecules made of the target unit repeated end to end, built from inverted repeats (a sequence followed by its reverse complement, which is what allows the folding). On an agarose gel they appear as a ladder or smear of bands. That pattern is the signature of a true LAMP product, and it is a useful way to tell it apart from background.

The webinar describes this growth as "factorial". Most sources call it exponential. Whichever word you prefer, the practical point is the same: you get an enormous amount of DNA, in micrograms rather than the milligrams the transcript suggests, which is why you can sometimes see the result by eye.

The design rules, with the reasons

None of these rules is arbitrary, so I will give the reason for each.

Primer length and orientation. Most primers (F3, B3, F2, B2, F1c, B1c, loops) are 15 to 25 nucleotides, much like a PCR primer. Primers are always written, ordered and synthesized 5' to 3', so you can paste the sequence straight into an order form.

GC content: 40 to 60%. G-C pairs have three hydrogen bonds and A-T pairs have two, so GC-rich DNA binds more tightly. Too little GC and the primer binds weakly. Too much encourages secondary structures.

Tm (melting temperature). The temperature at which half of a DNA duplex has separated, so it tells you how tightly a primer binds. Primers in a pair (F3 and B3, F2 and B2, and so on) should be within 5 °C of each other, so both work equally well at the reaction temperature.

No runs of three or more identical bases. Stretches like AAA, or dinucleotide repeats like ATAT, cause mispriming (binding slightly off target) and make the primer harder to synthesize and to clean up by HPLC (a purification method that removes truncated, failed copies of the oligo).

Avoid secondary structure. This is the big one for LAMP, because six primers means a lot of possible partners. The three troublemakers are:

• Hairpin: one primer folds back and pairs with itself.
• Self-dimer: two copies of the same primer stick together.
• Primer-dimer (cross-dimer): two different primers stick together. This is the main cause of non-specific background.

ΔG (Gibbs free energy) is how we score these. A more negative ΔG means a more stable structure. Here the webinar's own explanation tangled itself, and the speaker corrected the slide in the Q&A, so here is the version to remember:

• For dimers and hairpins, you want weak, unstable structures, so a ΔG that is not very negative. The webinar's guide is about −3.5 kcal/mol as the limit.
• For the ends of primers, you want the opposite: stable ends, with a ΔG of about −4 kcal/mol or lower, so the primer reliably starts and holds an extension. The ends that matter are the 3' end of F2, B2, F3 and B3, and the 5' end of F1c and B1c.

Poly-T linkers. A short run of T bases between the two halves of FIP or BIP gives the loop more flexibility. The speaker says it has been reported to add speed, but he has not been able to prove it himself. Treat it as something to try, not a rule.

Choosing the target (this is where false results are born)

A false negative is a real target you miss, and a false positive is a signal from something that is not your target. Both start at target selection.

Pick a conserved region, a stretch of sequence identical across all the strains you want to detect, found by multiple sequence alignment (lining up many strain sequences to see where they agree). The webinar showed seven Ebola strains with a short, perfectly conserved region, and eleven Drosophila strains too variable to design against.

If variation is unavoidable you have two options. You can use degenerate nucleotides, bases that stand for a mix using IUPAC codes (R means A or G, Y means C or T), so one primer covers both variants. Or you can design two primer sets for two populations.

Then check cross-reactivity, meaning your primers amplifying a related organism. BLAST, the database search tool, is a good start, but it only knows what is in its database. The speaker's advice is to also pick real near-neighbour organisms that could turn up in your sample, and test against them in the lab.

Software: PrimerExplorer and its quirks

The free workhorse is PrimerExplorer v5, from Eiken. It has real limits: sequences up to 2,000 base pairs, only A, C, G and T (so no degenerate codes, which you must add manually later), and results in HTML only. The most surprising quirk is that it designs the four main primers first, and the loop primers in a second, separate run.

A few practical points from the walkthrough:

1. Upload your conserved sequence and highlight any known mutation sites.
2. Default mode avoids mutations completely. Common mode buries them where a mismatch is tolerated (the 5' ends of F3, B3, F2, B2 and the 3' ends of F1c, B1c), so wild type and mutant both amplify. Specific mode places the mutation at the 3' end of F2, B2, F3, B3 (and the 5' end of F1c, B1c), so only the mutant amplifies, because a 3' mismatch blocks extension. Do not expect a perfect light switch. Test it.
3. From the five sets generated, choose one with the highest ΔG for dimerization, check the end stability, and make sure there is enough space between F2 and F1c for loops.
4. Save the set, reopen PrimerExplorer, load it, and run the loop primer design in the gaps.

If you get thousands of designs, tighten the settings. If you get none, widen the distances, or go back and find another conserved region. The software sorts your sequence into a GC-rich, AT-rich or normal bucket and sets defaults accordingly, which you can change under detailed settings.

A paid alternative from Premier Biosoft does alignments, interprets BLAST results automatically and calculates energies for multiplexing, which means detecting several targets in one tube.

The tip I found most useful: match the Tm to your enzyme

PrimerExplorer's defaults were built around Bst, which prefers 60 to 65 °C. Lucigen's LavaLAMP enzyme prefers 68 to 74 °C. The webinar showed what happens when you design with Bst defaults and run at the higher temperature: some published primer sets worked beautifully, and others gave zero separation between positives and negatives.

So they raised the design Tm set-point. The result was faster, more consistent time to result (TTR), which is when a reaction first crosses the detection threshold, and a larger gap between positives and negatives. Increasing the Tm by up to 6 °C helped. Some designs only allowed 4 or 5 °C, because the primers became too long, but even that improved things. Note that this data comes from one enzyme and the vendor's own experiments, so it is a lesson in thinking, not a universal law.

Optimization: screen first, then be clever

LAMP design is less predictable than PCR. The speaker's advice is blunt: design three to five sets, and be ready to go to six, because many will fail. His example showed three sets: one total failure, one with fast but noisy background, and one that gave positives in under ten minutes with almost no background. And do this with software, not by hand.

Then tune the reaction:

• Primer ratio: a common starting point is 1:4:8 (outer : inner : loop). If one primer causes trouble, lower its concentration.
• Reaction temperature: the biggest lever.
• Mg²⁺ (magnesium): the cofactor the polymerase needs. More speeds the reaction but also raises background. Lowering it is a fix for non-specific amplification.
• Additives: betaine reduces secondary structure and Triton is a detergent, but check whether they survive lyophilization, the freeze-drying that lets a reaction be shipped as a stable pellet for field use.

For serious optimization, use design of experiments (DoE), which changes several factors together in a planned way instead of one at a time. Start with a screening design to see which factors matter, then run a factorial design to see interactions, since the best Mg²⁺ level depends on temperature and enzyme amount.

Detection and its catch

You can read LAMP in real time with a fluorescent dye that lights up when bound to double-stranded DNA, which shows exactly when positives and background rise. You can also read it at the end by colour, using a pH-sensitive dye that changes colour as amplification acidifies the reaction (Tanner et al., 2015), or by turbidity, since the by-product pyrophosphate binds magnesium and forms a white precipitate (Mori et al., 2001).

Here is the catch. You cannot use hydrolysis (TaqMan) probes, because Bst lacks the exonuclease activity that cleaves them. So most LAMP detection is not sequence-specific, and primer-dimer background looks exactly like real signal. That is why no-template controls and gel checks matter so much.

The takeaway

LAMP gives you speed and simplicity at the bench, and charges you in the design stage. Use a conserved target, use software, make several designs, match your Tm to your enzyme, and let real-time data decide. A practical protocol is covered in detail in the Tomita et al. paper (Tomita et al., 2008).

Watch the video

This post is adapted from the Lucigen webinar on LAMP primer design and assay optimization: https://www.youtube.com/watch?v=GJkvQqDufh0

References

Mori, Y., Nagamine, K., Tomita, N., & Notomi, T. (2001). Detection of loop-mediated isothermal amplification reaction by turbidity derived from magnesium pyrophosphate formation. Biochemical and Biophysical Research Communications, 289(1), 150-154. https://doi.org/10.1006/bbrc.2001.5921

Nagamine, K., Hase, T., & Notomi, T. (2002). Accelerated reaction by loop-mediated isothermal amplification using loop primers. Molecular and Cellular Probes, 16(3), 223-229. https://doi.org/10.1006/mcpr.2002.0415

Notomi, T., Okayama, H., Masubuchi, H., Yonekawa, T., Watanabe, K., Amino, N., & Hase, T. (2000). Loop-mediated isothermal amplification of DNA. Nucleic Acids Research, 28(12), e63. https://doi.org/10.1093/nar/28.12.e63

Tanner, N. A., Zhang, Y., & Evans, T. C. (2015). Visual detection of isothermal nucleic acid amplification using pH-sensitive dyes. BioTechniques, 58(2), 59-68. https://doi.org/10.2144/000114253

Tomita, N., Mori, Y., Kanda, H., & Notomi, T. (2008). Loop-mediated isothermal amplification (LAMP) of gene sequences and simple visual detection of products. Nature Protocols, 3(5), 877-882. https://doi.org/10.1038/nprot.2008.57

Related Content

• Specificity vs sensitivity in CRISPR diagnostics: cry wolf, miss the wolf
• Sequencing platforms series: Sanger, Illumina, Nanopore, PacBio, Ion Torrent
• RNA extraction from Tempus blood tubes

 

Cry Wolf or Miss the Wolf? Specificity and Sensitivity in CRISPR Diagnostics

I have run enough diagnostic assays to know one thing for certain: a result on the screen is never the whole story. Somewhere behind every positive and every negative sits the question of whether the test got it right.

Think of a village watching for a wolf. Every time the village relies on a lookout, that lookout gets two chances to be wrong. The lookout can cry wolf, or the lookout can miss the wolf. A diagnostic test is no different.

Crying wolf: the false positive

There was no wolf, but the test said it saw one. That is a false positive, and the property that protects you from it is called specificity.

A highly specific test only raises the alarm when the target is truly there. A poorly specific one panics over shadows. In a clinic, that can mean unnecessary treatment, anxiety, and a lot of wasted resources.

Missing the wolf: the false negative

Now the opposite. The wolf was right there, but the test did not detect it. That is a false negative, and the property that protects you from it is called sensitivity.

This is the quieter failure. Nobody panics, which is exactly the problem. A patient who is infected goes home reassured, and the infection moves on.

How CRISPR helps with both

In a recent interview, Feng Zhang, one of the pioneers of CRISPR technology, explained that CRISPR-based diagnostics can improve both metrics at once. His reasoning is worth unpacking, because the two improvements come from two different steps.

Better sensitivity (fewer missed wolves). The sample is first concentrated and then amplified, usually by PCR or an isothermal method. CRISPR then adds a second layer of amplification on top, called collateral activity. Once the CRISPR enzyme recognises its target, it starts cutting nearby nucleic acids indiscriminately, including a reporter molecule that gives off a signal. The result is a stronger signal from the same amount of starting material. It is a bit like turning up the volume on a whisper.

In the SHERLOCK platform that Zhang’s group developed, this enzyme is Cas13, which targets RNA (Gootenberg et al., 2017). Zhang does not name the enzyme in this interview, so that detail comes from the original paper.

Better specificity (fewer false alarms). Amplification already checks for the viral sequence once. CRISPR then checks again, because the guide RNA has to recognise the sequence before the signal is produced. A false result has to survive both checks to count. It is the friend who asks “are you sure?” before you hit send.

Why this matters outside the lab

Zhang points out that CRISPR diagnostics are being developed for point-of-care and at-home use. For anyone working in places where a full molecular lab is not around the corner, that is the part that excites me most. A test that is both sensitive and specific, and that does not need a lab full of equipment, changes who gets an answer and how quickly.

Quick summary

The wolf	What it means	Technical term	How CRISPR helps
Cry wolf (Specificity)	It was not there, but the test said it saw it	False positive (specificity problem)	Target checked twice: amplification, then CRISPR detection
Miss the wolf (Sensitivity)	It was there, but the test did not detect it	False negative (sensitivity problem)	Amplify first, then Cas13 collateral cutting boosts the signal

Related Content

CRISPR-Cas9 Explainer: How It Works, Guide RNA and PAM: https://adwoabiotech.blogspot.com/2026/11/crispr-cas9-explained-guide-rna-pam.html

Reference

Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., Verdine, V., Donghia, N., Daringer, N. M., Freije, C. A., Myhrvold, C., Bhattacharyya, R. P., Livny, J., Regev, A., Koonin, E. V., Hung, D. T., Sabeti, P. C., Collins, J. J., & Zhang, F. (2017). Nucleic acid detection with CRISPR-Cas13a/C2c2. Science, 356(6336), 438–442. https://doi.org/10.1126/science.aam9321

CRISPR-Cas9 Explained: How It Works, Guide RNA and PAM

 

CRISPR-Cas9 Explained: From Bacterial Immunity to Genome Editing

CRISPR-Cas9 has gone from a curiosity in bacterial genomes to one of the most powerful tools in molecular biology. In this post, we look at where it came from, how the system works, and the key terms you will meet when you start designing guide RNAs, using sickle cell disease as our worked example.

Where CRISPR came from

The story starts with an odd observation. Researchers sequencing microbial genomes (bacteria and archaea) kept finding clusters of short, repeated sequences, first noticed in E. coli in 1987. The repeats were separated by short, unique stretches of DNA called spacers. The name CRISPR, short for clustered regularly interspaced short palindromic repeats, was coined in 2002. (The repeats are only partly palindromic, but the name stuck.)

The breakthrough came in the mid-2000s, when several groups realised that the spacers match the genomes of bacteriophages (viruses that infect bacteria) and plasmids. That led to a striking idea: spacers are a molecular memory. When a bacterium survives an infection, it stores a snippet of the invader's DNA as a new spacer, flanked by repeats. In 2007, experiments confirmed that CRISPR is an adaptive immune system in bacteria.

How the bacterial system works

The CRISPR array is transcribed and processed into short crRNAs, each carrying one spacer plus part of a repeat. In the type II system, each crRNA pairs with a second RNA, the tracrRNA, and the RNA pair loads onto a Cas (CRISPR-associated) protein. If the same virus returns, the crRNA recognises its matching DNA and the Cas protein cuts it, stopping the infection.

There are many Cas proteins, and they are numbered. Not all of them cut DNA: Cas13, for example, targets RNA, and Cas1 and Cas2 insert new spacers into the array. The best known and most widely used is Cas9, a DNA-cutting enzyme. In 2012, Jinek, Charpentier, Doudna and colleagues showed that Cas9 could be programmed to cut any DNA sequence of choice, and Charpentier and Doudna shared the 2020 Nobel Prize in Chemistry for it.

Key terms

Cas9. An RNA-guided DNA endonuclease. On its own it cannot choose where to cut. The guide RNA tells it.

Guide RNA (gRNA). In bacteria, the guide is two separate RNAs (crRNA and tracrRNA). In the lab, they are fused into a single sgRNA of about 100 nucleotides (nt). Because RNA is single-stranded, we count it in nucleotides, not base pairs.

The 20-nt targeting sequence. The 5' end of the sgRNA, 20 nt long, is the part you design. It pairs by complementarity with one strand of the target DNA, the target strand. Change those 20 nt and you re-aim Cas9 at a different place in the genome.

PAM (protospacer adjacent motif). A short sequence next to the target that Cas9 must see before it will cut. For the Cas9 from Streptococcus pyogenes, the PAM is NGG (N is any base), located immediately 3' of the 20-nt target on the non-target strand. Cas9 recognises the PAM first, then unwinds the DNA and checks whether the guide matches. This is also why the PAM limits where you can edit: you can only target sites that have an NGG next to them.

What Cas9 actually does

Once Cas9 is bound to the guide and the target, it makes a blunt double-strand break about 3 bp upstream of the PAM. It is important to understand that Cas9 only cuts. The edit itself comes from the cell's own repair machinery:

• NHEJ (non-homologous end joining) is fast and error-prone, and often leaves small insertions or deletions (indels) that disrupt a gene.
• HDR (homology-directed repair) can copy a supplied donor template to make a precise change, but it is much less efficient, particularly in non-dividing cells.

Which pathway wins will shape what kind of edit you can realistically make.

Worked example: sickle cell disease

Sickle cell disease is a good model for thinking about guide design because the cause is a single, well-characterised mutation. It lies in the gene for the haemoglobin subunit beta, HBB. A single adenine is replaced by a thymine (A>T; c.20A>T), which changes the codon GAG to GTG on the coding strand. As a result, glutamic acid is replaced by valine at position 6 of the beta chain (Glu6Val, or p.Glu7Val in current HGVS nomenclature, which counts the initiator methionine). That one amino acid change makes haemoglobin S polymerise when deoxygenated, distorting red blood cells.

The first step in designing a guide is to get the sequence:

1. Go to NCBI Entrez and search for the gene responsible for the disease, in this case HBB.
2. Open the gene record and retrieve the reference sequence around the mutation site.
3. Look for NGG PAM sites close to the mutation, then choose a 20-nt sequence next to each one as a candidate guide.

In the next post, we will go through choosing and checking candidate guides.

A reality check on therapy

A word of caution before anyone imagines Cas9 simply "fixing" the sickle cell mutation. Correcting a point mutation by HDR is inefficient, and that is why the first approved CRISPR therapy for sickle cell disease (Casgevy, approved in 2023) takes a different route. Rather than correcting HBB, it cuts the erythroid enhancer of BCL11A to switch fetal haemoglobin back on, which compensates for the faulty adult haemoglobin. Base editors cannot directly revert T to A, but adenine base editors have been used to convert the sickle codon into a benign variant (Hb G-Makassar), and prime editing offers another route to the precise correction.

Key takeaways

• CRISPR is a bacterial adaptive immune system: spacers store a memory of past infections.
• Cas9 is guided by an RNA; in the lab, the two bacterial RNAs are fused into a single sgRNA of about 100 nt.
• The first 20 nt of the sgRNA are the part you program, and the target must sit next to an NGG PAM.
• Cas9 cuts about 3 bp from the PAM; the cell's repair pathways, NHEJ or HDR, decide the final edit.
• Sickle cell disease comes from a single A>T change in HBB, which makes it an excellent worked example.

How Direct RNA Sequencing Works on Oxford Nanopore Pores

Reading RNA the way nature made it

Most RNA sequencing begins with a small act of conversion. You take your RNA, convert it into cDNA with reverse transcriptase, amplify it, and only then do you read it. It works beautifully, and I have used it for years, but every one of those steps leaves fingerprints on the data.

Direct RNA sequencing skips all of that. The native RNA molecule itself goes through the pore. No reverse transcription, no PCR, nothing in between.

The idea is not as new as it might feel. Oxford Nanopore described the approach in a 2016 preprint, and the direct RNA protocol reached customers in April 2017. The full paper appeared in Nature Methods in 2018 (Garalde et al., 2018). Since then the chemistry has moved on, and the current kit is SQK-RNA004, which replaced RNA002.

Three questions come up whenever I explain this to students. How is each pore measured? How many molecules can one pore read? And how much can you get from a whole flow cell?

How is each pore measured individually?

Picture a flow cell as a tiny array of wells. Each well holds a membrane with one protein nanopore embedded in it, and each well has its own electrode wired to a custom chip (an ASIC).

The chip holds a constant voltage across the membrane and measures the ionic current in picoamps, thousands of times per second. For RNA004 the sampling rate is about 4 kHz.

When an RNA molecule carrying a motor-protein adapter is captured, it partly blocks the pore and the current drops to a characteristic level. The motor then ratchets the strand through, 3' end first, at roughly 130 nucleotides per second. As it moves, the current steps up and down depending on which nucleotides (and which modifications) sit in the narrowest part of the pore.

Because every channel has its own electronics, every channel records independently and at the same time. The raw signal is saved per read, tagged with the channel number and read ID, in POD5 format (older runs used FAST5). Basecalling software such as Dorado then converts the signal into sequence.

How many RNA molecules can one pore read?

One at a time. That is the simple answer.

When a strand finishes, it is released, the current returns to the open-pore level, and the pore is free to capture the next molecule. So is there a fixed limit? Not in principle. In practice, the limit is how long the pore stays healthy.

Pores slowly degrade, or get blocked by debris, damaged RNA or secondary structure. The software tries to rescue a blocked pore by briefly reversing the voltage, which sometimes works and sometimes does not. A 1 to 2 kb mRNA takes only about 10 to 15 seconds to read, so a good pore can get through hundreds to a few thousand molecules over a 48 to 72 hour run. Longer RNAs and dirtier samples mean fewer. These are rough figures, and they vary a lot from pore to pore and library to library.

How many molecules can one flow cell read?

That depends on which flow cell you hold in your hand.

• MinION or GridION flow cell: about 2,048 pores sitting on 512 channels. The software only reads 512 at a time. Using a "mux scan" it picks the best pore in each group, then switches to others as the first ones die, so the pores are used up gradually across the run.
• PromethION flow cell: around 3,000 pores across 2,675 channels, with far higher total output.
• Flongle: 126 channels, a small and cheap option that is ideal for testing a library before committing.

For direct RNA on a MinION flow cell, a typical run gives somewhere between one million and several million reads, depending on RNA length, library quality and how many pores stay alive. Treat that as a ballpark, because your own run report is the best guide.

One small note on terminology that trips people up. The kit is the library prep chemistry, and the flow cell is the consumable that carries the pores. They are bought separately, and yield depends on both.

Why bother with all this?

Because the native molecule carries information that cDNA throws away. Direct RNA reads can span a full-length transcript, which helps with isoforms, transcription start sites and alternative polyadenylation. They also let you measure poly(A) tail length on each individual read, and they preserve RNA modifications such as m6A and pseudouridine (Jain et al., 2022; Workman et al., 2019).

The trade-offs are real. You need a fair amount of input (hundreds of nanograms of poly(A)+ RNA), and per-read accuracy is lower than short-read sequencing, although RNA004 narrowed that gap considerably.

Watch the video

Direct RNA sequencing on Oxford Nanopore explained: https://youtu.be/ra1L-pYHXts?si=ymfHktlFAmm-sDzq

Related Content

• PacBio vs Oxford Nanopore: https://adwoabiotech.blogspot.com/2026/06/pacbio-smrt-sequencing-vs-oxford.html

• New to sequencing technologies? Start with Sanger Sequencing Explained: How One of the Oldest DNA Sequencing Methods Still Works to understand the foundational method that long-read platforms eventually built on. 

  If you want to see how short-read NGS stacks up against Sanger, check out What Is Next-Generation Sequencing? NGS vs Sanger Explained. 

  And for a closer look at how Nanopore sequencing compares to the original Sanger method, read What Is Oxford Nanopore Sequencing? How It Differs from Sanger.

References

Garalde, D. R., Snell, E. A., Jachimowicz, D., Sipos, B., Lloyd, J. H., Bruce, M., Pantic, N., Admassu, T., James, P., Warland, A., Jordan, M., Ciccone, J., Serra, S., Keenan, J., Martin, S., McNeill, L., Wallace, E. J., Jayasinghe, L., Wright, C., Blasco, J., Young, S., Brocklebank, D., Juul, S., Clarke, J., Heron, A. J., & Turner, D. J. (2018). Highly parallel direct RNA sequencing on an array of nanopores. Nature Methods, 15(3), 201–206.

Jain, M., Abu-Shumays, R., Olsen, H. E., & Akeson, M. (2022). Advances in nanopore direct RNA sequencing. Nature Methods, 19(10), 1160–1164.

Workman, R. E., Tang, A. D., Tang, P. S., Jain, M., Tyson, J. R., Razaghi, R., Zuzarte, P. C., Gilpatrick, T., Payne, A., Quick, J., Sadowski, N., Holmes, N., de Jesus, J. G., Jones, K. L., Soulette, C. M., Snutch, T. P., Loman, N., Paten, B., Loose, M., Simpson, J. T., Olsen, H. E., Brooks, A. N., Akeson, M., & Timp, W. (2019). Nanopore native RNA sequencing of a human poly(A) transcriptome. Nature Methods, 16(12), 1297–1305.

How to Thaw Plasmodium falciparum Cultures: Step by Step

You pull a vial out of liquid nitrogen, thaw it, wash it, set up the flask, and then you wait. Day one, the smear is empty. Day two, still nothing. Did you just waste a precious stabilate? Almost certainly not, but the anxiety is real, and it is the reason this post exists.

Thawing Plasmodium falciparum is not difficult. It is, however, unforgiving in a few very specific places, and knowing where those places are makes all the difference between a culture that bounces back and one that quietly disappears. Today I will walk you through the full procedure for a stabilate frozen in glycerolyte, explaining the reason behind each step as we go.

A short detour: why glycerol, and why so much care?

The cryoprotective power of glycerol was discovered by accident in 1949, when a bottle on a lab shelf turned out to hold glycerol rather than the albumin its label promised, and the sperm samples stored in it survived freezing. Parasitologists later borrowed the idea, and glycerol-based freezing solutions became the standard way to bank malaria parasites.

Here is the catch. Glycerol protects red cells from ice damage by sitting inside them at high concentration. When you thaw the vial, that glycerol is still in there, so if you suddenly dilute the cells in plain medium, water rushes in faster than the glycerol can leave and the cells burst. The whole thaw is designed around one idea: take the glycerol out gently, by stepping the salt concentration down in stages.

What you need before you start

Warm everything to 37°C (or at least room temperature) and have your fresh O+ red cells washed and ready before the vial leaves the freezer. Scrambling for reagents mid-thaw is how mistakes happen.

Reagent	Volume (per 1 mL of stabilate)	What it does
12% NaCl	0.2 V (200 µL)	First osmotic step, draws water out of the cells before glycerol leaves
1.8% NaCl	10 V (10 mL)	Second step, lets the cells relax towards normal tonicity
0.9% NaCl / 0.2% glucose (optional)	10 V (10 mL)	Gentler final step, useful for old or low-parasitaemia stabilates
Incomplete medium (RPMI 1640)	10 V (10 mL) per wash	Removes the remaining salt
Complete medium	2 to 3 mL	Culture start, with Albumax or serum

V is simply the volume of your thawed stabilate. A typical vial holds about 1 mL, so 0.2 V is 200 µL, but scale everything to your own vial.

Step by step

Step 1: Thaw quickly. Take the vial from liquid nitrogen (or -80°C) and warm it in a 37°C water bath or in your hand until it has just thawed. Wipe the vial with ethanol and move it into the biosafety cabinet. Slow thawing lets ice crystals regrow and shred the cells, so speed matters here.

Step 2: Transfer and measure. Pipette the stabilate into a sterile 15 mL tube and note the volume. That number is your V.

Step 3: Add 12% NaCl drop by drop. Add 0.2 V (about 200 µL for a 1 mL vial) one drop at a time, flicking or swirling the tube gently between drops. Then wait 5 minutes. If I could put a flashing warning on any step, it would be this one. The cells are at their most fragile right now, and dumping the salt in quickly is the single most common cause of a failed thaw.

Step 4: Add 1.8% NaCl slowly. Add 10 V (about 10 mL), dropwise at first and then a little faster as the tube fills, mixing as you go. Spin at 500 × g for 5 minutes.

Step 5: Remove the supernatant. Aspirate carefully and leave a little liquid above the pellet, because the pellet will be small and easy to suck up by mistake. If your stabilate is old, or you suspect the starting parasitaemia was low, add 10 mL of 0.9% NaCl / 0.2% glucose here, mix gently, and spin again at 500 × g for 5 minutes.

Step 6: Wash. Resuspend the pellet in 10 mL of incomplete medium, spin at 500 × g for 5 minutes, and remove the supernatant. Repeat once more if you like. Two gentle washes are plenty.

Step 7: Start the culture. Resuspend the pellet in 2 to 3 mL of complete medium and add fresh red cells to reach about 3 to 5% haematocrit. This step is not optional. A thawed stabilate contains very little blood, and without fresh cells the parasites that survive have nowhere to invade. Keeping the volume small also keeps parasites and red cells close together, which helps that first invasion.

Step 8: Gas and incubate. Gas the flask with 5% CO₂, 5% O₂, 90% N₂ (or use a candle jar if that is what your lab has) and incubate at 37°C.

The waiting game

Change the medium every day, gently, without stirring up the cells that have settled at the bottom. Make a Giemsa smear at 24 hours and again at 48 hours, but do not panic if both are empty. Recovery is rarely dramatic. Parasites often reappear only after 3 to 5 days, once the first round of reinvasion has finished and the numbers are high enough to find under the microscope.

Once you can see parasites, keep the culture at 2 to 5% haematocrit and subculture with fresh red cells when parasitaemia reaches about 1 to 3%. Resist the urge to synchronise or otherwise stress the culture until it is growing steadily. A culture that has just come back from the freezer deserves a quiet few weeks.

When things go wrong

Nothing after 7 to 10 days? The stabilate may have been of low quality or frozen at a very low parasitaemia. Before giving up, keep the culture going and add fresh red cells every week. Some stabilates are simply slow.

Lots of debris or haemolysis in the first pellet? The salt went in too fast, or the vial thawed too slowly. Next time, slow down the 12% NaCl step and use fewer, smaller drops.

Parasites visible but not expanding? Look at your Albumax or serum lot and check the pH of your medium. Growth problems after a successful thaw are very often medium problems in disguise.

Contamination? Discard it promptly and thaw a second vial if you have one.

The short version

Thaw fast, add salt slowly, wash gently, give the parasites fresh red cells, and be patient. If you remember only one thing, remember the 12% NaCl step. It is the moment the whole procedure turns on.

Related Content

• How to Isolate Red Blood Cells for Plasmodium Culture
• The Plasmodium falciparum Life Cycle Explained
• How to Calculate Hematocrit for Passaging
• Giemsa Stain: Morphology and History
• DNA Sequencing Platforms Compared: Sanger, Illumina, Nanopore, PacBio

Plasmodium falciparum WGS: How to Prepare Red Blood Cells

A few weeks ago I put together a video on how to isolate and wash red blood cells for Plasmodium falciparum culture. It covers plasma removal, buffy coat removal, three washes, and a tidy 5% hematocrit at the end. Then a question landed that I had not planned for: what changes if the culture is going to be used for whole genome sequencing (WGS)?

The honest answer is: quite a lot. I expected a tweak or two. What I got was a rethink of nearly every stage, from the donor blood right through to the way the parasites are harvested. So let's walk through it together.

Why does sequencing change the rules?

For a normal culture, a few stray white blood cells are a nuisance at worst. They do not stop the parasites from growing, and nobody is counting them. Sequencing is a different story, because every cell in the flask contributes DNA to the tube, and not all DNA is equal.

Think about the numbers. The P. falciparum genome is only about 23 million base pairs. A single human white blood cell carries roughly 6 billion. That means one leukocyte holds around 250 times more DNA than a haploid ring-stage parasite. A handful of survivors from the buffy coat can quietly take over your sequencing reads, and you only find out after you have paid for the run. Painful.

Step 1: Remove white blood cells properly

In the standard video protocol we aspirate the buffy coat and move on. For WGS, that is not enough. Pass the blood through a leukocyte-depletion filter such as a Plasmodipur filter, or through a cellulose column such as CF11 (Auburn et al., 2011; Venkatesan et al., 2012). This is the single biggest change on the list.

But there  is another part that is easy to miss. It applies to the uninfected donor red blood cells you add to the culture, not only to patient blood. If the donor cells carry white blood cells, those white cells come along for the ride into every passage.

Step 2: Be choosy about the donor blood

Use fresh, well-characterised donor blood, ideally group O positive, and aim to use it within about 2 weeks of collection. Keep the washing steps from the original protocol: three washes in incomplete RPMI 1640. Where you can, use the same red blood cell batch across related cultures, so that batch-to-batch variation does not muddy the comparison.

Step 3: Keep the culture short

The longer parasites sit in a flask, the more they adapt to it. Culture adaptation can select for particular clones, and it has been linked to loss-of-function mutations and changes in subtelomeric and var gene regions (Claessens et al., 2017). If you are sequencing fresh patient isolates and want to preserve the original diversity, keep it to a few cycles.

Then ask yourself a simple question: do you want one genotype or many? Limiting dilution gives you a single clone, which makes variant calling much cleaner. A mixed infection gives you several clones in one tube, which is biologically interesting but harder to untangle.

Two housekeeping points while you are here:

• Test the culture for mycoplasma. Contamination adds foreign DNA that you do not want in the library.
• If you need a defined stage, synchronise with 5% sorbitol for about 10 minutes at 37 °C. Rings carry one genome copy, while late schizonts carry many and give you more DNA per cell.

Step 4: Harvest with saponin, not just a pellet

In the culture-only workflow, you simply keep the cells growing. For DNA, you need to get rid of the red blood cell contents first. Lyse the red blood cells with saponin at about 0.05% to 0.15% for roughly 5 minutes. This frees the parasites while the haemoglobin and most host material stay in solution.

Next, spin the parasites down (typically 2,000 to 3,000 g for 5 to 10 minutes) and wash several times in cold PBS until the supernatant runs clear. Adjust the speeds to your own centrifuge and tubes. For the best yield, harvest at high parasitaemia, around 5% or more, when the parasites are at late trophozoite or schizont stage.

Step 5: What if the DNA yield is low?

Sometimes you simply cannot grow enough parasites, especially with field isolates at low parasitaemia. There are two main rescue routes:

• Selective whole genome amplification (sWGA) preferentially amplifies parasite DNA over human DNA (Oyola et al., 2016). It is very helpful for low-input samples, but it can introduce coverage bias, so it is not ideal if copy number or structural variants are your focus.
• CpG-methylation-based host depletion, for example with the NEBNext Microbiome DNA Enrichment kit, takes advantage of a neat fact: human DNA is CpG-methylated, while P. falciparum DNA is not.

Step 6: Check before you commit

Quantify your DNA with a fluorometric method such as Qubit, not NanoDrop, because NanoDrop overestimates in the presence of contaminants. Then check the human-to-parasite ratio with qPCR using a human target and a Pf target. A small pilot run before the full sequencing batch can save a lot of money and heartache.

Culture versus WGS at a glance

Stage	Routine culture	Culture for WGS
White cell removal	Remove buffy coat	Buffy coat removal plus Plasmodipur or CF11 filtration
Donor blood	Washed three times	Fresh, filtered, same batch where possible
Culture length	As long as needed	As short as possible
Harvest	Keep cells growing	Saponin lysis, cold PBS washes
Quality control	Giemsa smear	Qubit plus human-to-parasite qPCR, mycoplasma test

Watch the video

If you would like to see the standard red blood cell isolation workflow first, the full video is here: Isolating Red Blood Cells for Plasmodium Culture. It is the foundation for everything above, and the WGS changes slot neatly on top of it.

The take-home message

Culture asks, "Are my parasites happy?" Sequencing asks, "Whose DNA is in this tube?" Once you think about it that way, the changes make sense: filter the white cells, keep the culture short, lyse with saponin, and check the human-to-parasite ratio before you spend a cent on sequencing.

References

Auburn, S., Campino, S., Clark, T. G., Djimde, A. A., Zongo, I., Pinches, R., Manske, M., Mangano, V., Alcock, D., Anastasi, E., Maslen, G., Macinnis, B., Rockett, K., Modiano, D., Newbold, C. I., Doumbo, O. K., Ouédraogo, J.-B., & Kwiatkowski, D. P. (2011). An effective method to purify Plasmodium falciparum DNA directly from clinical blood samples for whole genome high-throughput sequencing. PLoS ONE, 6(7), e22213.

Claessens, A., Affara, M., Assefa, S. A., Kwiatkowski, D. P., & Conway, D. J. (2017). Culture adaptation of malaria parasites selects for convergent loss-of-function mutants. Scientific Reports, 7, 41303.

Oyola, S. O., Ariani, C. V., Hamilton, W. L., Kekre, M., Amenga-Etego, L. N., Ghansah, A., Rutledge, G. G., Redmond, S., Manske, M., Jyothi, D., Jacob, C. G., Otto, T. D., Rockett, K., Newbold, C. I., Berriman, M., & Kwiatkowski, D. P. (2016). Whole genome sequencing of Plasmodium falciparumfrom dried blood spots using selective whole genome amplification. Malaria Journal, 15, 597.

Venkatesan, M., Amaratunga, C., Campino, S., Auburn, S., Koch, O., Lim, P., Uk, S., Socheat, D., Kwiatkowski, D. P., Fairhurst, R. M., & Plowe, C. V. (2012). Using CF11 cellulose columns to inexpensively and effectively remove human DNA from Plasmodium falciparum-infected whole blood samples. Malaria Journal, 11, 41.

Related Content

• How to Isolate Red Blood Cells for Plasmodium Culture
• The Plasmodium falciparum Life Cycle Explained
• How to Calculate Hematocrit for Passaging
• Giemsa Stain: Morphology and History
• DNA Sequencing Platforms Compared: Sanger, Illumina, Nanopore, PacBio