Natural killers

Why natural killer cells?

The most recent (evolutionarily) arm of innate immunity, NK cells, were devised to counter a vulnerability in our immune system. The usual scheme of things  - before NK cells -  was something like this: you got your B lymphocytes and antibodies to fight off bacteria and ward off parasites. Viruses, however, often found there way inside cells and colonised cells, effectively turning the cell into some primitive version of a zombie, chrning out baby viruses thousands by the minute. Cellular immunity, i.e. T lymphocytes, eventually found a way to deal with those pesky viruses. Viral particles (antigens) were picked up by a molecule called "MHC class I" and presented to T cells, as a distress signal. The T cell could then recognise that particle, and smelling the rot inside, unleashed its destruction machinery on the cell. The cell dies, and so does the virus inside it, everybody loses. 

All this was done and dusted very early in evolution though, until some smart teenage mutant ninja virus came up with a devilish idea: why don't attack the cell nucleus and turn off the very expression of MHC class I molecules, effectively rendering the infected cell invisible to T lymphocytes. The virus could then safely reside, and multiply (divide?) in the cell, without being 'seen' by T lymphocytes.

The situation continued for a while, until of course, animals one-upped the virus in the arms race. NK cells appeared first in chordates, as an update patch to plug this very hole. NK cells are large lymphocytes which patrol the body and look for MHC class I molecule. The mechanism is really simple: if a cell does not express MHC class I, NK cells assume the worst (that it's an infected cell, or a cancer cell) and kills the cell with its arsenal of enzymes. Really, a no-brainer solution for smart viruses. This has worked well for us throughout evolution, although indiscriminate killing isn't good strategy in the long term (chronic hepatitis and autoimmune diseases, anyone?), as we're only recently beginning to understand.

In normal course of things, cytotoxic T cells (CD8+) have no difficulty in identifying virally infected cells because MHC class I molecules bring out those foreign, viral proteins to the surface

If a virus has learnt to turn of MHC class I expression, it is invisible to T cells; however, the NK cell notices the absence of MHC class I on the surface, and kills the cell

Identifying NK cells

NK cells are identified by their low/ variable expression of CD8 and a multifunctional molecule CD56, also called 'neural cell adhesion molecule' (NCAM).

Initial forward and side scatter plot of peripheral blood mononuclear cells prepared with Lymphoprep; lymphocytes constitute a healthy 29% of mononuclear cells (the debris at the bottom left constitute the other majority)

Within the lymphocytes, distribution of CD8 shows three peaks - the negative ones at the left (possibly helper T cells, B cells and macrophages), a dim population in the middle, and a bright one at the right (cytotoxic T cells); the later two constitute 48% of all lymphocytes

CD56 is expressed at higher levels in the dim population, suggesting that the dim population is where NK cells reside

Majority of NK cells express CD56 only dimly (the ones in the box, which turn around to be 20.62% of CD8+ cells, and 9% of all lymphocytes in this particular case). A few of them show bright  CD56 (above the box)

Ref: William G. Morice, MD, PhD. The Immunophenotypic Attributes of NK Cells and NK-Cell Lineage Lymphoproliferative Disorders. (DOI: 10.1309/Q49CRJ030L22MHLF)

Konstantin Khalturin, Matthias Becker, Baruch Rinkevich, Thomas C. G. Bosch. Urochordates and the origin of natural killer cells: Identification of a CD94/NKR-P1-related receptor in blood cells of Botryllus. Proceedings of the National Academy of Sciences Jan 2003, 100 (2) 622-627; DOI: 10.1073/pnas.0234104100

Myst animals

What goes on in the mind when its still not much more than a lump of earth ? When the synapses are still in making, when words are only conceived in fragments, and uninhibited thoughts (madhyama) are directly transmitted to the hands? Dreamscapes and nightmares take shape in the sketches of a toddler. 

Little things in the lab

 

Stairs, alleys and labyrinths

Rajasthan, 2021

 

Perspectives

 

Flow cytometry I: Compensation

Put simply, as long as we don't find fluorochromes with absolute fidelity (i.e. very narrow emission peaks), we simply have to compensate raw readings from a flow cytometer. Let's start wth just two fluorochromes in two channels, 

1. Phycoerythrin (PE) in FL1 detector (red)

2. Fluorescein isothiocynate (FITC) in FL2 detector (yellow)

From a purely theoretical physics perspective, FITC and PE show a consistent and reproducible  overlap in their emission spectrum (from Spectra analyser; https://www.biolegend.com/en-us/spectra-analyzer)

Looking at the two emission spectrum (X axis is wavelength in nm), one can calculate how much FITC emission overlaps with PE, and vice versa; it is evident that FITC has a significant component where PE peak (red) is located, but PE does not have much of a tail where FITC (yellow) peaks

Usually flow cytometers have detector which detect light within a band of wavelengths, and they are calibrated to detect certain peaks. For example, a flow cytometer might have a red detector (FL1) and a yellow detector(FL2).

By simple eyeballing, one can guess that 15% of the red peak is contributed by the yellow tail, which is very close to the correct figure

Looking at the graph, we can guess that the emission peak of PE is augmented by 15% because of 'spillover' from FITC. This percentage is quite invariant in the abstract, ideal world of theoretical physics. In such an ideal world, for each cell which is interrogated by the flow cytometer, we can deduce the 'true' PE emission by the equation:

PE_real = PE_observed - (0.15 x FITC_observed)

The graph also shows a 1% contribution of PE in FITC peak (the remnant of the red tail in the yellow band). Thus,

FITC_real = FITC_observed - 0.01 x PE_observed

The two equations are simultaneous, i.e. they have two unknown variables PE_real and FITC_real, and thus can be solved (the flow cytometer has already provided you with PE_observed and FITC_observed). In general, for n fluorochromes to be detected by n bands, you will have n such equations; with a little linear algebra, you can manually solve them all (or ask your favorite Flow Cytometry analyser software to do it for you!). All you need is a ready made compensation matrix, which is an assortment of the correction factors in the form of an n x n table.

	FITC	PE
FITC	1	0.15
PE	0.01	1

Is that it?

Unfortunately, no. Flow cytometers are real instruments who measure real cells with real lasers and detectors, which are prone to voltage fluctuations, dead cell artifacts, and errors which simply can not be traced to a particular cause. Plus, each flow cytometer can have a different detector and different band pass. Thus, using the blanket values 15% & 1% is not advised. It is now routine to determine these values every time.

Now how do you do that? Spectrometry is not everyone's cup of tea. Fortunately, we can make an assumption that will make the task simpler: fluorescence intensity in a channel is the sum of contributions from all fluorochromes, in the same ratio in which their emission spectra overlap. That is to say, if I get an intensity x in the FL1 channel and y in FL2 channel, I can be sure that some fixed fraction of x is always contributing to y.

Determination of this correction factor, now, becomes a matter of designing an experiment. We prepare 

1. a set of cells (C1) stained only with FITC combined with a diffusely positive marker (CD3)
2. another set of cells (C2) stained only with PE tagged to the same CD3

Now let's run C1 in our flow cytometer, and plot FITC vs PE. Ensure that

1. No part of the negative population does not go off the left of the chart, i.e. it must fully stay within the chart (adjust the voltage if necessary)
2. The positive population is at least as positive as you would expect cells to be in your eventual experiment
3. You don't use different cell lines (i.e. don't put lymphocytes in C1 and monocytes in C2; in such cases, better to use custom made beads which bind all IgG kappa antibodies for this purpose)

In this uncompensated graph, find the median fluorescence intensity (MFI) of both FITC and PE channel (although you haven't actually put PE in this tube, just FITC; whatever PE is showing is due to spill from FITC)

For example, let's say 

• MFI_FITC_Pos = 80
• MFI_FITC_Neg = 10
• MFI_PE_Pos = 30
• MFI_PE_Neg = 5

You can now make a few simple calculations

• Let x = (MFI_PE_Pos - MFI_PE_Neg) = (30-5) = 25, which represents the effective MFI in PE channel
• Let y = (MFI_FITC_Pos - MFI_FITC_Neg) = (80-10) = 70, which is the effective MFI in FITC channel

Now the factor x/y = 25/70 = 35% is the correction factor for spill of FITC into PE. You can now similarly calculate correction factor for spill of PE into FITC from the C2 tube.

Note that after compensation, the width of the FITC positive population will increase slightly; this is because the FITC information is now coming from two sources, both FITC and PE, thus increasing its error (degree of freedom); however, the median PE negative line (blue) must remain the same through both populations

With n fluorochromes, one can try n tubes with cells/ beads, each having a single fluorochrome, and acquire and plot them, and do the maths by hand, to come up with a compensation matrix. Or, you know, just let your favorite software do it magically.

Engraftment monitoring: part I

Following transplant of hematopoeitic stem cells, one must carefully follow up the patient for months. This is because the aim of a bone marrow transplant is to reconstitute the immune system, or - to be more precise - to borrow some immunity from a donor. Which means, eventually, that at some point of time, most of immune system (i.e. the white blood cells) of the recipient has to be replaced entirely by the donor's. In essence, the recipient will become a genetic chimera, carrying a distinct cell population with the donor's genome.

Now, how do you know when that happens? White blood cells look all alike under the microscope, and there is no telling whether it belongs to the patient or donor just by looking at them. Which brings us to they key question of identity, i.e. how am I any different from you?

If the donor and recipient are of opposite gender, a simple karyotype from a sample of blood cells would easily identify which one is from whom. The Y chromosome will stand out. However:

1. If you have ever done cytogenetics, you know the mind-numbing labor that goes into karyotyoing

2. If multiple donors of different genders have donated stem cells to a single recipient (as is often the case), the method falls on it face.

So let's move on to genetic differences between two individuals. As members of the same species, we are more or less similar, except for a few things. Short tandem repeats (STR) are repetitions of a short nucleotide core (usually less than 9 nucleotides), which vary in repeat numbers between people. For example the donor might have:

GTCGTCGTCGTC

is a 4-repeat of a 3-sequence. Again, the recipient could have:

GTCGTCGTCGTCGTCGTCGTC

is a 7-repeat of the same. Which is to say that if you perform a polymerase chain reaction for this particular STR, the band for the donor and the recipient would show up at different places.

For small differences like a few nucleotides, gel electrophoresis is too low res. On capillary electrophoresis, the same would show up as two peaks.

Capillary electrophoresis of STR PCR; the exact height and area of the peaks are subject to PCR conditions, the loci being chosen and the relative difference in repeats between the two

Remember: PCR is competitive; shorter segments will eat up reagent nucleotides faster than longer ones (simply because the longer repeats need more time to copy). In this case, the difference 7-4=3 is not much; but if you pick a VNTR (variable number of tandem repeats) which are much more variable in repeats than STR, the difference can show up and skew the analysis in favor of the shorter allele.

Like every other gene, STRs are also inherited in two alleles: one maternal and one paternal. A person might have

• homozygous at an STR locus, i.e. both alleles might have the same number of repeats
• heterozygous, i.e. one allele has a 7-repeat, another a 3-repeat (i.e. the aforementioned scenario)

In an ideal scenario, the donor and the recipient have completely different STR alleles: maybe the donor has a 7-repeat and a 4-repeat, and the recipient has a 10-repeat and a 15-repeat. Which makes things really simple to analyse:

The proportion of donor cells in this case is simply the proportion of donor alleles (a & b) in the post transplant mix (green), i.e. 

The height/ area of the peaks reflect the relative proportion of the alleles in the mix (remember: all are competing for the same primer!)

(a + b)/ (a + b + c + d)

This simplest scenario is of course, quite idealistic. One won't often find such an STR between two individuals; and even if one does, it is not wise to assess engraftment by only one STR marker. Consider this particular situation where the exact same allele pair is carried by both donor and recipient.

This is a non-informative STR marker - because the donor and recipient carry the same allele pair

In other situations, there might be one common allele between the two.

The allele at extreme right is ahred between the recipient; the one at extreme left belongs to donor only. Thus, the proportion of the left allele in the final sample is the amount of chimerism

This is akin to the schema:

Here, the amount of chimerism is b / (b + d), i.e. pretend that 'a' is not there.