Plate map maker for 96- and 384-well plates

$$C_k=\frac{C_0}{f^k},k=0,1,\dots ,n-1$$$$\text{wells needed}=n\times r$$$$\text{inner wells}=(\text{rows}-2)\times (\text{columns}-2)$$

Plates and well names. Microplates follow the ANSI/SLAS footprint and well-position standards: rows are lettered from the top (A, B, C …) and columns numbered from the left, so the well in row B and column 3 is B3. Some plate readers write the column with two digits (B03); the CSV export offers both.

Dilution series. A serial dilution by a factor $f$ from a start concentration $C_0$ gives $C_k=C_0/f^k$ at step $k$; $n$ points span a range of $f^{n-1}$ (64-fold for seven 2-fold steps). The helper writes each well’s own concentration and can add a zero point (a blank or zero standard). Lower concentrations are drawn lighter, on a log scale, so the series reads at a glance. To work out the transfer volumes, use the dilution calculator.

Replicates. With $r$ replicates a layout needs $n\times r$ wells. Side by side puts the copies in neighbouring lanes (the classic ELISA layout: S1 in A1 and A2, S2 in B1 and B2); next in line puts them one after another along the series; interleaved places the whole set once and then again, so replicates sit apart and a local plate effect does not hit all copies of one sample. The helpers fill the selected wells, or the next empty wells in column or row order when nothing is selected, and refuse (with the number of wells missing) rather than place a partial layout.

Edge effect. Outer wells lose more medium to evaporation and change temperature faster than inner ones, so cells grow and signals drift at the edges (Lundholt et al. 2003). In SW480 cells, outer-well metabolic activity was about 35% lower than in central wells in one brand of plate (Mansoury et al. 2021). The edge option fills the outer ring with a buffer group that you fill with medium or buffer and do not measure; the table above shows how many wells remain.

Randomisation. Plate position effects (rows, columns, edges, gradients from pipetting order or incubator position) are common in plate assays (Brideau et al. 2003; Malo et al. 2006). Randomising which well each sample goes into keeps those effects from lining up with your conditions. The tool shuffles the contents of a block with the Fisher–Yates algorithm (Durstenfeld 1964) driven by the mulberry32 generator from a numeric seed: the same seed, block and contents always give the same plate, so the layout can be reproduced from the seed in your notebook. Buffer wells never move; by default standards, controls and blanks stay where they are too (untick that to randomise the controls as well).

Controls. Screening assays put positive and negative controls on every plate, often in the first and last columns, to measure the assay window and $Z^{\prime }$ (Zhang et al. 1999); keep them away from the outer ring when you use an edge buffer (the helper does this for you).

Files. The long CSV has one row per well (well, row, column, condition, concentration, unit, replicate, type), the tidy layout analysis software joins to plate-reader output by well. The grid CSV writes one plate-shaped block per variable (condition, concentration, unit, replicate, type), with the variable name in the top-left cell and a blank line between blocks, the layout read by the plater R package (Hughes 2016). Both import back. The share link stores the plate, compressed, in the part of the address after #, which browsers do not send to the server.

Sources: Lundholt BK, Scudder KM, Pagliaro L. A simple technique for reducing edge effect in cell-based assays. J Biomol Screen 2003;8:566–570, doi:10.1177/1087057103256465. Mansoury M, Hamed M, Karmustaji R, Al Hannan F, Safrany ST. The edge effect: a global problem. The trouble with culturing cells in 96-well plates. Biochem Biophys Rep 2021;26:100987, doi:10.1016/j.bbrep.2021.100987. Brideau C, Gunter B, Pikounis B, Liaw A. Improved statistical methods for hit selection in high-throughput screening. J Biomol Screen 2003;8:634–647, doi:10.1177/1087057103258285. Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R. Statistical practice in high-throughput screening data analysis. Nat Biotechnol 2006;24:167–175, doi:10.1038/nbt1186. Zhang JH, Chung TDY, Oldenburg KR. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 1999;4:67–73, doi:10.1177/108705719900400206. Durstenfeld R. Algorithm 235: Random permutation. Commun ACM 1964;7:420, doi:10.1145/364520.364540. Hughes SM. plater: Read, tidy, and display data from microtiter plates. J Open Source Softw 2016;1:106, doi:10.21105/joss.00106.

Sandwich ELISA on a 96-well plate

1. Standards. Select A1:G2 (or leave nothing selected), choose the Standard group, start 1000 pg/mL, factor 2, 7 points, duplicates side by side, down columns. $C_k=1000/2^k$ gives the series below.

2. Samples. Select C1:K8 and place 24 samples in triplicate, side by side, down columns: $24\times 3=72$ wells, Sample 1 in A3, A4 and A5, Sample 9 in A6, A7 and A8.
3. Controls. Paint the positive control in A12:C12 and the negative control in D12:F12.
4. Check and print. 94 of 96 wells are used (16 standards and blanks, 72 samples, 6 controls), leaving G12 and H12 empty. Print the bench sheet, tape it next to the plate, and export the long CSV to join with the reader’s ODs; then fit the curve with the ELISA standard curve calculator. This layout is the ELISA template in the tool.

384-well single-concentration screen

Switch on the edge buffer: the outer ring of $2\times 16+2\times 24-4=76$ wells becomes buffer, leaving $14\times 22=308$. Put DMSO (negative control) in the first inner column (B2:O2, 14 wells) and the positive control in the last (B23:O23, 14 wells), then fill the remaining 280 wells with compounds. With 14 wells of each control, the plate gives a $Z^{\prime }$ from 28 control wells while no measured well sits on the edge.

Randomising sample positions

Twenty samples in triplicate placed in order fill B2:G11, with Sample 1 in B2, B3, B4. Randomising that block with seed 20261110 moves Sample 1 to C8, E10, G4; anyone who enters the same seed for the same block gets exactly this plate.