www.elsevier.com/locate/jim

Journal of Immunological Met

Research paper

Detection of anti-brain serum antibodies using a semi-quantitative

immunohistological method

Sabrina Boscolo a, Monica Passoni a, Valentina Baldas b, Iacopo Cancelli c,

Marios Hadjivassiliou d, Alessandro Ventura b, Enrico Tongiorgi a,*

a BRAIN Centre for Neuroscience, Department of Biology, University of Trieste, Via L. Giorgieri, 10, 34127 Trieste, Italyb Pediatric Clinic and Transfusional Centre of the IRCCS Burlo Garofolo, Trieste, Italy

c Ospedale Santa Maria Maggiore, Department of Clinical Neurology, Udine, Italyd Department of Clinical Neurology, The Royal Hallamshire Hospital, Sheffield, UK

Received 21 July 2005; received in revised form 31 October 2005; accepted 28 November 2005

Available online 4 January 2006

Abstract

The number of autoimmune disorders that may involve the nervous system is increasing. The diagnosis of neurological

involvement in the context of systemic diseases may be helped by the detection of autoantibodies reacting against neural

autoantigens. If the autoantigen is not known but the target tissue is suspected, immunohistochemistry is one of the main

techniques used to certify the presence of autoantibodies. Autoreactive antibodies are also present in the healthy population but in

low quantity compared to patients with such diseases. Quantification of such autoantibodies could help to discriminate between

disease and healthy states. We have developed a densitometric immunohistological method for the evaluation of human serum anti-

neural reactivity. Using a densitometric analysis of rat brain sections incubated with the serum from 107 healthy subjects, we have

defined the baseline of natural anti-neural autoreactivity, and the cut-off for subsequent quantification of anti-neural reactivity in

patients with neurological involvement in the context of autoimmune diseases, including systemic lupus erythematosus, para-

neoplastic cerebellar degeneration, and stiff person syndrome. The test sensitivity was 81% with a positive predictive value of 52%,

a specificity of 89% with a negative predictive value as high as 97%. In conclusion, this standardised semi-quantitative procedure

makes immunohistochemistry a reliable diagnostic test for autoimmune neuropathologies and represents an excellent exclusion test

for anti-neural autoimmunity.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Immunohistochemistry; Densitometry; Image analysis; Natural autoimmunity; Neuroimmune pathologies; Central nervous system

0022-1759/$ - s

doi:10.1016/j.jim

Abbreviation

autoimmune ne

bert–Eaton myas

peripheral nervo

erythematosus.

* Correspondi

E-mail addr

hods 309 (2006) 139–149

ee front matter D 2005 Elsevier B.V. All rights reserved.

.2005.11.020

s: AEA, anti-neodymium antibodies; AGA, anti-gliadin antibodies; anti-tTG, anti-tissue transglutaminase antibodies; AIND,

urological disorder; CNS, central nervous system; GBS, Guillain–Barre syndrome; HBD, healthy blood donors; LEMS, Lam-

thenic syndrome; MG, myasthenia gravis; NAA, natural autoantibodies; PBS, phosphate buffered saline; PBST, PBS-Tween; PNS,

us system; RT, room temperature; SD, standard deviation; SE, standard error; SPS, stiff person syndrome; SLE, systemic lupus

ng author. Tel.: +39 040 5583864; fax: +39 040 568855.

ess: tongi@units.it (E. Tongiorgi).

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149140

1. Introduction

The nervous system can often be involved in auto-

immune diseases. In addition to well-defined autoim-

mune disorders of the nervous system such as

myasthenia gravis (MG), Lambert–Eaton myasthenic

syndrome (LEMS), Guillain–Barre syndrome (GBS),

paraneoplastic cerebellar degeneration (Vincent et al.,

1999), there is also an increasing number of less well

characterised autoimmune disorders (Lang et al., 2003).

Autoantibodies can be very valuable in the diagnosis of

well-established autoimmune disorders of the nervous

system (e.g. acetylcholine receptor antibodies in myas-

thenia gravis). For the less well characterised autoim-

mune neurological disorders, as well as for relatively

common autoimmune disorders of the central nervous

system, such as multiple sclerosis, there are no diag-

nostic laboratory tests available. To detect the presence

of autoantibodies in the serum, the laboratory test is

very much dependent on the availability of target anti-

gens. If the target antigen is not known but is suspected,

immunohistochemistry is the main starting point to

determine if there is an antibody–antigen interaction.

However, it is well known that autoreactive antibodies,

named natural autoantibodies (NAA), exist in the

healthy population and they are identical to those

found in autoimmune diseases. Although the opposite

case, i.e. that any autoantibody found in autoimmune

diseases is also present in healthy individuals, may not

always apply, natural and pathological autoantibodies

are known to use the same V genes, have the same

extent of mutation and have the same affinity and

specific reactivity (reviewed in Lacroix-Desmazes et

al., 1998). The most important difference between

these antibodies in health and disease is the different

quantity and epitope fine specificity (Lacroix-Desmazes

et al., 1998). Thus, quantifying immunohistochemical

reactivity by a semi-quantitative method may allow

autoantibodies to be measured and make immunohisto-

chemistry a more reliable diagnostic test to distinguish

natural from pathological autoreactivity.

Immunohistochemistry is one of the oldest methods

of providing information about antibody–antigen inter-

actions. One of the main problems with this technique

is that reproducibility and visual interpretation of the

pattern obtained is highly operator-dependent. To im-

prove the problem of reproducibility, several instru-

ments that automate antibody pipetting, incubation

and washing steps together with chromogen detection

are available from several companies (e.g. Biogenex,

DakoCytomation, Lab Vision, Ventana Medical Sys-

tems, Vision BioSystems). Moreover, some recently

developed scanning optical devices that are used to

analyse immunohistochemistry on tissue sections or

tissue microarrays have software that allows automated

image capture and analysis (e.g. Nikon Instruments,

Carl Zeiss). In order to define the critical features in

terms of immunohistochemical patterns and staining

intensity, which could allow natural anti-brain reactivity

to be distinguished from a true pathological autoim-

mune reaction against the CNS, we have developed a

simple method to analyse digital images of immuno-

histochemical staining on rat brain sections.

2. Patients and methods

2.1. Patients and healthy blood donors

The healthy donors (n =107; 57 males, 50 females)

had no previous history of autoimmune disorders even

in their first- and second-order relatives. All were blood

donors at the Pediatric Hospital Burlo Garofolo of

Trieste (Italy) and had therefore been periodically tested

for several years. Anti-tissue transglutaminase (anti-

tTG) ELISA (Sblattero et al., 2000), anti-gliadin

(AGA, Alpha-gliatest S-IgA and S-IgG; Eurospital,

Trieste, Italy) and anti-endomysium (AEA) (Not et

al., 1997) autoantibody tests were negative. Neurolog-

ical patients (n=16; 4 males, 12 females) were selected

among patients with autoimmune neurological disor-

ders (AIND) including systemic lupus erythematosus

(SLE) with neurological involvement (n =8; 2 males, 6

females), SLE (n =3 females), paraneoplastic cerebellar

degeneration (n =4; 1 male, 3 females), and stiff person

syndrome (SPS) (n=1 male). Diagnosis of SLE was

performed according to the classification criteria pro-

posed by the American College of Rheumatology

(ACR) (Tan et al., 1982; Hochberg, 1997). Briefly, 11

criteria are considered by the ACR to make the diag-

nosis: malar rash, discoid rash, photosensitivity, oral

ulcers, arthritis, serositis, renal disorder, neurologic

disorder, haematologic disorder, immunologic disorder

and abnormal titre of anti-nuclear antibody. A patient is

said to be affected by SLE when four or more of the 11

proposed criteria are present simultaneously. Subjects

were considered to have neuropsychiatric lupus syn-

drome when they had three or more of the previous

criteria and they met the case definition for neuropsy-

chiatric lupus (American College of Rheumatology,

1999).

All SLE patients, with or without neurological com-

plications, were positive for anti-nuclear antibodies

(ANA) in the ELISA test (Microplate ELISA test sys-

tem ANA Screen, Euroimmun, Lubeck, Germany). One

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149 141

SLE patient with neurological complications was also

positive for IgG cardiolipin autoantibodies and one SLE

patient was positive for both IgG and IgM anti-cardio-

lipin autoantibodies (Microplate ELISA test system

AMA Screen, Euroimmun, Lubeck, Germany). The

diagnosis of paraneoplastic syndrome was made

according to the criteria described in Graus et al.

(2004). Of the four paraneoplastic patients, one was

positive for anti-Yo and one was positive for anti-Hu

antibodies (EUROLINE-WB, Euroimmun, Lubeck,

Germany). The other two patients were negative for

the known paraneoplastic antibodies but one patient

had paraneoplastic cerebellar degeneration due to

non-Hodgkin’s lymphoma and the other patient had

an aggressive prostatic carcinoma and was diagnosed

as paraneoplastic syndrome after a chest X-ray. The

SPS patient was positive for anti-glutamic acid decar-

boxylase (GAD) on RIA test (Euroimmun, Lubeck,

Germany). All patients and healthy blood donors

(HBD) gave consent.

2.2. Immunohistochemistry

Brains were obtained from adult Sprague–Dawley

rats of the breeding colony at the University of Trieste.

Procedures involving animals and their husbandry were

performed in accordance with national (DL N116, GU,

suppl 40, 18 December 1992) and international laws and

policies (European Community Council Directive 86/

609, Oja L 358, 1, 12 December 1987). Rat brains

were fresh-frozen on dry ice, sectioned in a cryostat

(Leica, Germany) and 10 Am sagittal sections were

collected on gelatin-coated slides (Sigma, Milan, Italy).

Sections were dried overnight at room temperature (RT)

and stored at �80 8C until required. The following

staining procedures were carried out manually or auto-

matically using a DakoCytomation autostainer (Dako-

Cytomation, Denmark). The serum from each patient or

healthy control was analysed on rat brain sections from

two different rats according to the previously published

method (Hadjivassiliou et al., 2002).

2.3. Densitometric analysis

To define a cut-off for the subsequent densitometric

analysis on rat brain sections, three steps were neces-

sary. In the first step, sections were labelled by immu-

nohistochemistry using sera from 107 healthy blood

donors. Colour images (RGB format) from three differ-

ent brain regions, including the cerebellum (Purkinje

cells), cortex (V stratum) and brainstem, were captured

with a Nikon DXM1200 video camera coupled to a

Nikon E800 microscope (Nikon-Italy srl, Florence,

Italy) at 40� magnification with Nomarski interference

optical filters. Second, images were converted into

grey-scale pictures with 8-bit codification (1280�1024 pixels), giving a signal intensity from 0 (darkest

pixel) to 255 (brightest pixel). For the densitometric

evaluation, the grey-scale codification was independent

of the section being looked at since it was calibrated

onto a reference grey-scale. Accordingly, the zero value

of the darkest pixel was assigned to the black field of

this reference grey-scale. In addition, to ensure that the

microscope illumination was always constant during

different measurement sessions, at the beginning of

each session the darkest pixel intensity was measured

on a reference labelled brain section (always the same).

Third, the value of the darkest pixel(s) mapping onto

recognizable neuronal cellular structures (i.e. cell soma

or processes) was determined for each picture. The

staining on blood vessels was not included in this

analysis. In this step, initially the function bCount/SizeQ under the bMeasureQ menu of the software pack-

age Image-Pro Plus (Media Cybernetics, Silver Spring,

MD) was used to visualize the distribution of the pixel

intensity values of each image. Then, the operator

moved the cursor manually to the option bSelect rangeQalong the pixel intensity histogram until the darkest

pixels of the displayed brain image were highlighted

on clearly recognizable cellular structures (cell process-

es or soma). The corresponding pixel intensity value

was read from the dialogue box and transferred into an

Excel worksheet and finally, the mean value of the

darkest pixels from the 107 healthy donor sera was

calculated. The mean value of the darkest pixels from

the 107 healthy donor sera minus 1SD defined the cut-

off. The staining intensity was then ranked in four

classes. When the staining intensity fell between 255

(white, means no signal) and the mean value �1SD, it

was considered as a negative signal. Borderline signal

intensities included values between �1 and �2SD;

positive values were between �2 and �3SD and

strongly positive values were between the mean value

�3SD and 0 (black, means maximal positive signal).

To perform the densitometric analysis, a pseudo-

colour (bPseudocolorQ function of the Image-Pro Plus

software) was assigned to the different staining inten-

sity ranges defined as above on the basis of the cut-off.

The pseudo-colours chosen to produce the highest level

of visual discrimination in the resulting image were: no

staining for negative values, blue for borderline, light

green for positive and red for strongly positive signals.

The images from the three different brain areas labelled

with the patient or healthy donor sera were first con-

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149142

verted into pseudo-colour images and then the operator

visually identified if pixels of the different colours were

present on cellular structures. The sera were ranked on

the basis of the strongest pixel intensity class identified

on neuronal cellular structures.

2.4. Immunocytochemistry

Commercial HEp-2 cell line (human, Caucasian,

larynx, carcinoma, epidermoid; Centro Substrati Cellu-

lari Istituto Zooprofilattico Sperimentale, Pavia, Italy)

was cultured in minimum essential medium with Ear-

le’s salts and Glutamax I (MEM, Invitrogen, CA) with

10% fetal bovine serum (FBS) in a 5% CO2 humidi-

fied atmosphere at 37 8C. Cells were carefully washed

with phosphate buffered saline (PBS), fixed for 15 min

at RT with paraformaldehyde (PFA) 4%/PBS and trea-

ted with PBS/glycine 0.1 M and then with PBS/Triton

0.1% at RT for 5 min. After one wash with PBS, cells

were incubated with patient and control sera at 37 8Cfor 45 min (dilution 1 /100 in PBS), washed twice in

PBS and incubated for 45 min at 37 8C with the

secondary antibody (anti-human IgG FITC conjugated,

dilution 1 /50 in PBS) (DakoCytomation, Denmark).

Evaluation of sera staining on HEp-2 was carried out

visually by two different observers who were unaware

of the identity of the serum under observation (double

blind procedure).

3. Results

3.1. Detection of anti-brain antibodies on rat brain

sections

The sera of the 107 HBD were tested by immuno-

histochemistry (for IgGs only) on unfixed sagittal rat

brain sections to determine the baseline anti-brain reac-

tion in the normal population. Theoretically, in unfixed

sections, soluble antigens may be at risk of being

washed out. However, labelling of unfixed section

with antibodies against soluble antigens such as GAD

revealed the presence of high levels of the antigen thus

excluding any relevant loss of these types of antigens

from unfixed sections (data not shown). The staining

procedure was carried out manually but we also

obtained comparable results when 20 sera were ana-

lysed automatically with the DakoCytomation autostai-

ner (data not shown). The immunohistochemical

staining pattern of the 107 HBD was compared to

that produced by the serum IgGs from the 16 patients

with autoimmune neurological disorders. As shown in

Fig. 1, the analysis was restricted to three representative

brain regions, cerebellum (high magnification of the

Purkinje cell layer; Fig. 1A,D), cortex (layer V; Fig.

1B,E) and brainstem (reticular neurons; Fig. 1C,F). Fig.

1A–C displays representative histochemical results

obtained with the serum IgGs (dilution 1 /600) of one

HBD showing only a slight background reactivity. In

marked contrast, Fig. 1D–F shows typical strong label-

ling on most neuronal perikarya and nuclei produced by

serum IgGs of one AIND patient affected by SLE with

CNS involvement. Each serum was tested on two rat

brain sections from two different rats (in total four

sections) to avoid intra-strain variability. No differences

among various rats were observed (data not shown).

3.2. Densitometric analysis

To determine the significance of the staining obtained

with the different sera, a densitometric analysis was

performed on the three rat brain areas shown in Fig. 1.

The colour pictures were converted into grey-scale

images (Fig. 2A–F; upper panels) and the pixel intensity

distribution was analysed (Fig. 2A–F; lower panels). In

general, when the three brain areas were labelled with

the sera from the HBD, the pixel intensity values were

restricted to a narrow range, i.e. between 180 and 240

(Fig. 2A–C; lower panels). In contrast, the pixel intensity

distribution obtained with the AIND sera was much

broader and less symmetric, showing a long tail towards

the darkest intensity values, even reaching a value of 27

(it is 53 for the example shown in Fig. 2D–F; lower

panels). In addition, the background of the sections

labelled with the AIND sera was found to be lower

than in sections incubated with the HBD sera and

showed pixel intensity values even above 250. The

consequence of this peculiar intensity distribution was

that the darkest pixels present on cellular neuronal struc-

tures in sections labelled with the HBD sera (see Section

2; red highlighted pixels in Fig. 2A–F, upper panels)

were much closer to the mean pixel intensity value of

the histogram (Fig. 2A–C; arrows in lower panels) than

in sections labelled with the serum from the patient with

SLE and neurological involvement (Fig. 2D–F; arrows

in lower panels) or the other AIND sera.

The darkest pixel intensity values for each HBD or

AIND patient in the three brain areas are shown in Fig.

3 (dispersion plot). The statistical analysis showed that

the difference in the mean values of the darkest pixel

intensity of the patient group was significantly different

with respect to the control group in all three brain areas

sampled (Kruskal–Wallis ANOVA on ranks; p b0.001)

and that nearly all AIND patients showed pixel inten-

sity with values below the mean intensity obtained for

Fig. 1. IgG immunohistochemistry on rat brain sections with serum from one healthy blood donor and a patient with systemic lupus erythematosus

with neurological involvement. The sera from one HBD and one patient with SLE with neurological involvement were incubated with unfixed adult

rat brain sagittal sections. Reacting antibodies were detected using an anti-IgG secondary antibody. Three brain regions were analysed: cerebellum

(ml, molecular layer; Pcl, Purkinje cells; gcl, granule cell layer in A, D), cortex (fifth layer, B, E) and brainstem (C, F) as indicated by letters in the

schematic picture of a rat brain (lower panel). The HBD serum (IgG) produced no or just background level staining, shown in A–C. On the contrary,

rat brain sections incubated with serum from the patient with SLE with neurological involvement showed strong labelling of most neuronal

perikarya and nuclei of each of the three areas (D–F). Calibration bar 40 Am.

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149 143

the whole healthy group. However, some HBD sera

also showed staining levels quite below the average

of the healthy group and some were even close to the

mean value of the AIND group. These data suggested

the need to rank the pixel staining intensities in differ-

ent categories. Therefore, starting from a cut-off value,

we created a further level of analysis to classify the

signal intensity into four different categories (negative,

borderline, positive or strongly positive; see Section 2).

The final outcome was to obtain, on the original grey-

scale pictures, a pseudo-colour overlay with three dif-

ferent colours using the bPseudocolorQ function of the

Image-Pro Plus software (Fig. 4A–F represents the

same neurons shown in Fig. 1, converted into pseudo-

colours; see also Section 2). Using these pseudo-

coloured images, the operator could easily assign the

staining on the three brain regions to the appropriate

class. The cut-off value was calculated on the basis of

the mean value of the darkest pixel intensity (MV) of

the sera from healthy blood donors, measured on Pur-

kinje cells, which was subtracted for its standard devi-

ation. All pixels between 255 and the cut off value were

scored as negative and were left unlabelled (Fig. 4A–F).

The pixels between the cut-off and the MV �2SD are

displayed in blue and represent the borderline staining;

the pixels between the MV �2SD and the MV �3SD

are highlighted in light green and correspond to a

positive staining, while all pixels below the MV

�3SD, displayed in red, represent a strongly positive

staining (Fig. 4D–F, compare with Fig. 1). Following

assignment of the serum staining on the different brain

areas to the different intensity classes, we found that the

large majority of healthy donors showed no or border-

line staining on Purkinje cells (cumulated negative+

Fig. 2. Example of detection of the darkest pixels on cellular bodies and processes labelled with the serum of one HBD or one AIND patient. Images

were converted from an RGB colour format into grey-scale pictures. In this format, each pixel has a grey value included between 0 (darkest pixel)

and 255 (brightest pixel). For each image, the distribution histogram of the pixel grey values is shown in the lower panels (A–F lower panels).

Labelling of the three brain areas with the HBD serum produced images with a narrow distribution of the pixel grey intensity, i.e. between 180 (A–C

arrows lower panel) and 240. Incubation of rat brain sections with the serum of the AIND patient represented in the figure produced a wider grey

intensity distribution ranging from 53 (D–F arrows lower panel) to 250. The darkest pixels were selected by the operator among those localised on

cell structures by setting the grey value threshold to a few pixels and these were highlighted in red on the grey-scale picture, as indicated by

arrowheads (A–F upper panel). The darkest pixel intensity values were recorded for each serum and those obtained from all the HBD (n =107) were

used to set the cut-off value for the pseudo-colour scale. Calibration bar 20 Am.

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149144

borderline=89.7%; Fig. 4G), cortex (93.5%; Fig. 4H)

and brainstem (60.7%; Fig. 4I) although in the brain-

stem a relatively high number of sera were positive or

strongly positive (39.3%, Fig. 4I). On the contrary, the

AIND patients were positive or strongly positive on

neurons of all areas (75.0% on Purkinje cells, 75.0% on

cortex and 93.8% on brainstem; Fig. 4G–I).

To determine to what extent our method was able to

detect natural autoimmunity in a sample of healthy

donors, we have compared it with a well standardised

immunocytological method (HEp-2 cells) which has

also been used in automated semi-quantitative evalua-

tion procedures (Perner et al., 2002). In addition, con-

sidering that 11 of the AIND patients were affected by

0

50

100

150

200

250

--- ---

---

--- ------d

arke

st p

ixel

val

ue

neg

bord

pos

s pos

*** ***

***

Purkinje cells Cortex Brainstem

HBD n=107 AIND n=16 HBD n=107 AIND n=16 HBD n=107 AIND n=16

Fig. 3. Distribution of the darkest pixel intensity values of the immunohistochemical staining with HBD and AIND sera in three rat brain areas. For

each image, the darkest pixel was identified on cellular structures, and its intensity value was recorded for the three brain areas from sections

incubated with either HBD or AIND sera. The dispersion plot shows that almost all HBD sera had a darkest pixel intensity value (triangles) greater

than the average of the darkest pixel intensity value of AIND sera (circles, the dotted line indicates the mean value and error bars indicate standard

error); all but three AIND sera had a darkest pixel intensity lower than the average of the darkest pixel intensity value of HBD sera (HBD dotted

line, error bars indicate standard error). The difference between the mean darkest pixel intensity of HBD versus AIND sera was statistically

significant in each of the three brain areas considered (***p b0.001 Kruskal–Wallis ANOVA on ranks).

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149 145

systemic lupus erythematosus, which is commonly di-

agnosed with quantification of ANA reactivity, and

since paraneoplastic sera may also be positive for

these antibodies (Aguirre-Cruz et al., 2005), we found

it of interest to compare the positivity of these sera with

the two methods. To this aim, a randomly chosen group

of HBD sera (n =16) and all the AIND sera (n =16)

were tested for ANA reactivity on the HEp-2 commer-

cial cell line and compared with reactivity on rat brain

sections. All but three HBD sera were negative with

both the HEp-2 and the densitometric analysis on rat

brain (Fig. 5A,B). Among the AIND samples, ten out of

16 sera gave a positive staining on HEp-2 cells while

six gave no staining. Three of the six sera, which were

negative on Hep-2 cells, were strongly positive using

the densitometric analysis on rat brain (Table 1). The

sera from the three AIND patients that were negative on

HEp-2 but positive on rat brain are shown in Fig. 5

(compare E, F or I, J or K, L). Representative AIND

patient sera, positive both on HEp-2 and rat brain

sections, are also shown in Fig. 5 (compare C, D or

G, H).

3.3. Test variables

On the basis of the results described above, we

calculated the test specificity, sensitivity, and the

negative and positive predictive values for the immu-

noreactivity on rat brain. Test specificity is the ratio

between all HBD sera that were negative after the

densitometry test (true negatives), and the whole

number of healthy blood donor sera tested (including

the ones that were positive after our test, called false

positive). This parameter is an index of the ability of

the test to detect as positive only the real patients.

We considered one HBD serum as true negative for

the rat brain test when at least two out of the three

brain regions were included in the negative or bor-

derline categories; an HBD serum was considered

false positive when two of the three brain regions

fell in the positive or strongly positive categories.

Using these limits we obtained a specificity of 95 /

95+12=89%.

The test sensitivity is defined as the ratio between

AIND patient sera that was positive with the test (true

positive) and all AIND patient sera (including those that

were negative after our test, called false negative); this

parameter indicates the ability of the test to detect all real

patients. We considered a patient serum as true positive

when at least two of the three brain regions were positive

or strongly positive and false negative when two of the

three brain regions were negative or borderline after the

densitometric analysis. Using these limits we obtained a

sensitivity of 13 /13+3=81%. Using the same criteria,

we also calculated the negative predictive value (NPV),

defined as the ratio of true negative / true negative+false

Fig. 4. Output of the pseudo-colour images and densitometric analysis of the serum immunohistochemistry. The signal intensity values were ranked

into different classes identified with different pseudo-colours (D–F). With this pseudo-colour scale, all pixels considered as negative (mean value of

the darkest pixel intensity of HBD sera minus its standard deviation) were not coloured (A–C), pixels considered borderline (between �1 and

�2SD) are indicated in blue, all positive pixels are visualized in light green (between �2 and�3SD) and the pixels considered strongly positive are

in red (less than �3SD). Each serum was classified in one of the four categories on the basis of the darkest pixel intensity values of the brain area

considered. The large majority of HBD sera had no (grey bars) or borderline (blue bars) staining on Purkinje cells (G) or cortex (H) while on

brainstem (I) some HBD sera were positive (light green bars) or strongly positive (red bars). In contrast, almost all AIND sera were positive or

strongly positive and only a few were negative or borderline in the three brain areas analysed (G–I). Calibration bar 20 Am.

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149146

negative; we obtained a value of 95 /95+3=97%. This

test parameter means that the large majority of the

negative sera belong to healthy donors. The positive

predictive value (PPV) is defined as the ratio between

true positive and true plus false positive sera; this pa-

rameter indicates that the majority of the positive sera

are sera from real patients. Our densitometric test had a

PPV of 13 /13+12=52%. The inter-assay reproducibil-

ity of the test was calculated by testing the same serum

ten times, each in quadruplicate (two sections from two

different animals) using the DakoCytomation automatic

immunostainer. The values obtained were: mean 157.22,

SD 16.12, CV 4.08%.

4. Discussion

The present study demonstrates that it is possible to

discriminate between natural and pathological anti-

brain reactivity using semi-quantitative immunohisto-

chemistry. This study aimed to describe and quantify

the natural reactivity against three regions of the rat

brain in sera from a group of 107 healthy blood donors.

Future research directions should include analysis of

the anti-brain reactivity of a larger positive control

group in more rat brain regions. Our final goal would

be to obtain a reactivity map sufficiently detailed to

discriminate between different neuropathologies.

Table 1

Comparison between positive (+) or negative (�) sera staining on rat

brain and on HEp-2

Serum Pathology Hep-2 Rat

brain

1 SLE with neurological involvement + +

2 SLE with neurological involvement � �3 SLE with neurological involvement � �4 SLE with neurological involvement + +

5 SLE with neurological involvement + +

6 SLE with neurological involvement � �7 SLE with neurological involvement + +

8 SLE with neurological involvement

and with mixed connective tissue disease

+ +

9 Systemic lupus erythematosus + +

10 Systemic lupus erythematosus + +

11 Systemic lupus erythematosus � +

12 Paraneoplastic cerebellar degeneration + +

13 Paraneoplastic cerebellar degeneration

carcinoma of prostate, paraneoplastic arthritis

+ +

14 Paraneoplastic cerebellar degeneration

with ovarian carcinoma

� +

15 Paraneoplastic encephalomyelitis,

lung carcinoma, very abnormal MRI,

peripheral neuropathy, ataxia, double vision

+ +

16 Stiff person syndrome/CD/D � +

CD, celiac disease; D, diabetes; SLE, systemic lupus erythematosus.

Fig. 5. Comparison between immunostaining of the HEp-2 cell line

and rat brain sections. To evaluate the efficacy of this semi-quantita-

tive immunohistochemical method in detecting anti-brain autoantibo-

dies, sera from randomly chosen HBD (n =16) and AIND patients

(n =16) were incubated both on HEp-2 cells and rat brain sections.

The large majority of HBD sera were negative both with HEp-2 (A)

and rat brain (B). Typical positive results, obtained after incubating

sera from patients with SLE with neurological involvement on HEp-2

cells, are shown in (C) and on rat brain in (D). Serum from a patient

with systemic lupus erythematosus, giving negative results on HEp-2

cells, is shown in (E). The same serum was positive on rat brain

sections (F). Similarly, the majority of sera from patients with para-

neoplastic syndrome showed strong labelling both on HEp-2 (G) and

rat brain (H) while one was negative on HEp-2 cells (I) but positive on

rat brain sections (J). The serum from a patient with stiff person

syndrome, celiac disease and diabetes was also negative on HEp-2

cells (K) but positive on rat brain (L). Calibration bar 40 Am.

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149 147

The use of the immunohistological method de-

scribed in this study, for diagnostic purposes, relies

upon the possibility of generating accurate and repro-

ducible immunohistochemical data using human sera.

We show that this method has an inter-assay variation

of only 4%, which is in line with the accuracy of most

commercial ELISA tests. We previously tested differ-

ent serum dilutions and found that the dilution range

producing the most accurate results was 1 /600 to 1 /

800 for IgGs and 1 /100 to 1 /200 for IgAs (Hadji-

vassiliou et al., 2002). The use of rodent brain sections

to test human autoantibodies that are supposed to react

with human brain antigens has been validated in a

number of previous studies from several groups, in-

cluding ourselves (Sillevis Smitt et al., 2000; Frassoni

et al., 2001; Hadjivassiliou et al., 2002; Singh and

Rivas, 2004). The choice of using frozen unfixed

sections is due to the consideration that any kind of

fixative may alter irreversibly the antigen conforma-

tion inducing the undesirable disappearance, or even

appearance of certain epitopes. Therefore, the frozen

unfixed sections as used in this study may allow the

serum antibodies to react with brain antigens in a

more bnative-likeQ conformation. In this study, the

unfixed rat brain sections appear to maintain their

antigenicity during the short-term incubation proce-

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149148

dures carried out either manually or automatically, as

demonstrated by the high preservation of the cellular

morphology (see this paper and also Hadjivassiliou et

al., 2002). The advantages of this method include the

fact that it allows automation of the procedure with

the ensuing possibility of comparing results from dif-

ferent laboratories. To generate the densitometric

results shown in this study, we have used proprietary

software, however we wish to point out that, in prin-

ciple, any other image analysis software could be

used, including bopen sourceQ software such as the

freely downloadable NIH imaging package (http://

www.rsb.info.nih.gov/ij). It is well known that some

antibodies in autoimmune neurological disorders, such

as in paraneoplastic syndromes, are specific to the

nervous system, as for example the anti-Yo (anti-Pur-

kinje cells antibodies) (Furneaux et al., 1990) or anti-

Hu (ANNA-1). For this reason, the use of neural

tissue or cell lines which express neural antigens is

mandatory. In principle, rat brains contain most if not

all the principal neural antigens involved in any auto-

immune disease. Thus, another important advantage of

this method is that it can be applied to any suspected

autoimmune neuropathology, as it does not require

knowledge and actual availability of the corresponding

antigen(s).

To determine to what extent our method is able to

detect natural autoimmunity we have compared it with

a well standardised immunocytological method using

immunofluorescence on cultured HEp-2 cells. It has

been previously demonstrated that immunofluorescence

on HEp-2 cells is poorly specific at a serum dilution of

1 /40 because it produces 31.7% of false positives in the

healthy population, but at a serum dilution of 1 /160 the

test becomes more specific excluding 95% of healthy

donors (Tan et al., 1997). For this reason, we have

chosen the working dilution of 1 /100 as an intermedi-

ate value between 1 /40 and 1 /160.

The most important result of this experiment is that

the number of sera from healthy donors that did not

stain on Hep-2 cells or rat brain sections was the same,

indicating that there is a good agreement between the

two methods in detecting natural autoimmunity. On the

other hand, this comparison also showed that sera from

HBD that are positive on Hep-2 cells are often also

positive on rat brain sections, at the serum dilutions

used in the present study, showing that the background

staining on unfixed rat brain sections is not higher than

that observed on fixed Hep-2 cells. Comparison of the

staining produced by AIND sera on the two substrates

showed that on the rat brain sections both ANA reac-

tivity as well as specific anti-neural reactivity can be

detected, supporting the view that most neural antigens

are expressed in rat brain. Our results showing a pos-

itive staining on rat brain with serum from a paraneo-

plastic patient positive for anti-Yo antibodies, which

was negative on HEp-2 cells, are in perfect accordance

with a recent study showing that the majority of sera

from patients with anti-Yo antibodies, but negative for

ANA, are negative on HEp-2 (Aguirre-Cruz et al.,

2005). The other two sera that gave a positive staining

on rat brain, but were negative on HEp-2, were from

one SLE and one SPS/CD/D patient. This staining

might be due to the presence of specific anti-neural

antibodies in these two sera.

It is becoming increasingly clear that many auto-

immune pathologies, including the neuropathologies,

can be the result of the presence of autoantibodies

against a multiplicity of antigens and that different

combinations of these multiple antibodies may account

for differences in the final clinical manifestations of a

certain disease (Naparstek and Plotz, 1993; Whitney

and McNamara, 1999; Archelos and Hartung, 2000;

Mocci et al., 2000). On this basis there is certainly the

need to develop new diagnostic tools to take into

account such complexity of the autoimmune patholo-

gies and several attempts to develop autoantigen

arrays are underway (Quintana and Cohen, 2004).

This implies that the most efficient strategy for the

diagnosis of the autoimmune basis of a disease would

be to use a diagnostic test with a broad specificity

rather than a test based on a single antigen. Standard-

ized, semi-quantitative immunohistochemistry may be

a valid broad specificity test and a less costly alterna-

tive than antigen arrays.

The main disadvantage of this method is that it is

operator-dependent. A further step towards automation

of the image analysis could be implemented through

development of an automatic pattern-recognition pro-

cedure. The analysis of the test showed that sensitivity

was 81% with a positive predictive value of 52%,

indicating that the test may give a large number of

false negatives. However, this apparent low perfor-

mance of the test is likely to be due to the restricted

number of AIND sera analysed since it is in marked

contrast with the very good results obtained with the

healthy donor population. In fact, the test analysis

gave a specificity of 89% with a negative predictive

value as high as 97%. Thus, the data obtained indi-

cate that this semi-quantitative immunohistological

method, if used as a diagnostic tool to detect auto-

immune anti-brain reactivity, may represent an out-

standing exclusion test for autoimmunity in neuro-

logical diseases.

S. Boscolo et al. / Journal of Immunological Methods 309 (2006) 139–149 149

Acknowledgements

This work was supported by Grant E1270 from

Telethon-Italy, Fondazione Cassa di Risparmio di

Trieste (ET and AV), Fondazione Kathleen Foreman

Casali, Trieste (ET), Progetto SISTER Regione FVG

(ET) and Fondi di ricerca corrente Ministero Sanita

(AV).

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